{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ames 房价数据探索\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np #线性代数\n",
    "import pandas as pd #数据处理，CSV文件读写\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14115</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Mitchel</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1.5Fin</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1993</td>\n",
       "      <td>1995</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>732</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>796</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>796</td>\n",
       "      <td>566</td>\n",
       "      <td>0</td>\n",
       "      <td>1362</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>480</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>143000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10084</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Somerst</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>Stone</td>\n",
       "      <td>186.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1369</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>317</td>\n",
       "      <td>1686</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1694</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1694</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>636</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>255</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10382</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NWAmes</td>\n",
       "      <td>PosN</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1973</td>\n",
       "      <td>1973</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>Stone</td>\n",
       "      <td>240.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>859</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>32</td>\n",
       "      <td>216</td>\n",
       "      <td>1107</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1107</td>\n",
       "      <td>983</td>\n",
       "      <td>0</td>\n",
       "      <td>2090</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>484</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>235</td>\n",
       "      <td>204</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>350</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6120</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>OldTown</td>\n",
       "      <td>Artery</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1.5Fin</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1931</td>\n",
       "      <td>1950</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>952</td>\n",
       "      <td>952</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>FuseF</td>\n",
       "      <td>1022</td>\n",
       "      <td>752</td>\n",
       "      <td>0</td>\n",
       "      <td>1774</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>8</td>\n",
       "      <td>Min1</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>468</td>\n",
       "      <td>Fa</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>129900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>190</td>\n",
       "      <td>RL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7420</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>BrkSide</td>\n",
       "      <td>Artery</td>\n",
       "      <td>Artery</td>\n",
       "      <td>2fmCon</td>\n",
       "      <td>1.5Unf</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1939</td>\n",
       "      <td>1950</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>851</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>991</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1077</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1077</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>1</td>\n",
       "      <td>205</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "5   6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6   7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7   8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8   9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9  10         190       RL         50.0     7420   Pave   NaN      Reg   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "5         Lvl    AllPub    Inside       Gtl      Mitchel       Norm   \n",
       "6         Lvl    AllPub    Inside       Gtl      Somerst       Norm   \n",
       "7         Lvl    AllPub    Corner       Gtl       NWAmes       PosN   \n",
       "8         Lvl    AllPub    Inside       Gtl      OldTown     Artery   \n",
       "9         Lvl    AllPub    Corner       Gtl      BrkSide     Artery   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "5       Norm     1Fam     1.5Fin            5            5       1993   \n",
       "6       Norm     1Fam     1Story            8            5       2004   \n",
       "7       Norm     1Fam     2Story            7            6       1973   \n",
       "8       Norm     1Fam     1.5Fin            7            5       1931   \n",
       "9     Artery   2fmCon     1.5Unf            5            6       1939   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "5          1995     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "6          2005     Gable  CompShg     VinylSd     VinylSd      Stone   \n",
       "7          1973     Gable  CompShg     HdBoard     HdBoard      Stone   \n",
       "8          1950     Gable  CompShg     BrkFace     Wd Shng       None   \n",
       "9          1950     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "5         0.0        TA        TA       Wood       Gd       TA           No   \n",
       "6       186.0        Gd        TA      PConc       Ex       TA           Av   \n",
       "7       240.0        TA        TA     CBlock       Gd       TA           Mn   \n",
       "8         0.0        TA        TA     BrkTil       TA       TA           No   \n",
       "9         0.0        TA        TA     BrkTil       TA       TA           No   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ         706          Unf           0        150          856   \n",
       "1          ALQ         978          Unf           0        284         1262   \n",
       "2          GLQ         486          Unf           0        434          920   \n",
       "3          ALQ         216          Unf           0        540          756   \n",
       "4          GLQ         655          Unf           0        490         1145   \n",
       "5          GLQ         732          Unf           0         64          796   \n",
       "6          GLQ        1369          Unf           0        317         1686   \n",
       "7          ALQ         859          BLQ          32        216         1107   \n",
       "8          Unf           0          Unf           0        952          952   \n",
       "9          GLQ         851          Unf           0        140          991   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "5    GasA        Ex          Y      SBrkr       796       566             0   \n",
       "6    GasA        Ex          Y      SBrkr      1694         0             0   \n",
       "7    GasA        Ex          Y      SBrkr      1107       983             0   \n",
       "8    GasA        Gd          Y      FuseF      1022       752             0   \n",
       "9    GasA        Ex          Y      SBrkr      1077         0             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710             1             0         2         1             3   \n",
       "1       1262             0             1         2         0             3   \n",
       "2       1786             1             0         2         1             3   \n",
       "3       1717             1             0         1         0             3   \n",
       "4       2198             1             0         2         1             4   \n",
       "5       1362             1             0         1         1             1   \n",
       "6       1694             1             0         2         0             3   \n",
       "7       2090             1             0         2         1             3   \n",
       "8       1774             0             0         2         0             2   \n",
       "9       1077             1             0         1         0             2   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "5             1          TA             5        Typ           0         NaN   \n",
       "6             1          Gd             7        Typ           1          Gd   \n",
       "7             1          TA             7        Typ           2          TA   \n",
       "8             2          TA             8       Min1           2          TA   \n",
       "9             2          TA             5        Typ           2          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn           2         548         TA   \n",
       "1     Attchd       1976.0          RFn           2         460         TA   \n",
       "2     Attchd       2001.0          RFn           2         608         TA   \n",
       "3     Detchd       1998.0          Unf           3         642         TA   \n",
       "4     Attchd       2000.0          RFn           3         836         TA   \n",
       "5     Attchd       1993.0          Unf           2         480         TA   \n",
       "6     Attchd       2004.0          RFn           2         636         TA   \n",
       "7     Attchd       1973.0          RFn           2         484         TA   \n",
       "8     Detchd       1931.0          Unf           2         468         Fa   \n",
       "9     Attchd       1939.0          RFn           1         205         Gd   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "5         TA          Y          40           30              0        320   \n",
       "6         TA          Y         255           57              0          0   \n",
       "7         TA          Y         235          204            228          0   \n",
       "8         TA          Y          90            0            205          0   \n",
       "9         TA          Y           0            4              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN    NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN    NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN    NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN    NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN    NaN         NaN        0      12    2008   \n",
       "5            0         0    NaN  MnPrv        Shed      700      10    2009   \n",
       "6            0         0    NaN    NaN         NaN        0       8    2007   \n",
       "7            0         0    NaN    NaN        Shed      350      11    2009   \n",
       "8            0         0    NaN    NaN         NaN        0       4    2008   \n",
       "9            0         0    NaN    NaN         NaN        0       1    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal     208500  \n",
       "1       WD        Normal     181500  \n",
       "2       WD        Normal     223500  \n",
       "3       WD       Abnorml     140000  \n",
       "4       WD        Normal     250000  \n",
       "5       WD        Normal     143000  \n",
       "6       WD        Normal     307000  \n",
       "7       WD        Normal     200000  \n",
       "8       WD       Abnorml     129900  \n",
       "9       WD        Normal     118000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#配置pandas DataFrame的显示\n",
    "pd.set_option('max_columns',100)\n",
    "#数据路径\n",
    "dpath = './data/'\n",
    "data=pd.read_csv(dpath+\"Ames_House_train.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n",
    "# 特征有很多是Object类型，需要处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#配置pandas DataFrame的显示\n",
    "pd.set_option('max_rows',100)\n",
    "#查看是否有空值\n",
    "#data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 探索数据\n",
    "查看和分析每个变量，决定时否使用该特征以及如何使用该变量，同时对数据行的有效性实现本判断"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pd.set_option('max_rows',100)\n",
    "#data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2rxZjcp0lljzW2X0SoOD3WADqK4KsnFfLq/tPMD4ZznY4xpgkLLHksXeOekNw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc87940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（房屋价格）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.SalePrice.values, bins=30, kde=True)\n",
    "#plt.xlabel('Median value of owner-occupied homes', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1 第1列 Id 为编号，对房价预测并无意义，可删除\n",
    "data=data.drop(['Id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc87400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2 第2列 MSSubClass 建筑类别 数值型，但代表类别信息，应用哑编码，此处删除\n",
    "sns.countplot(data.MSSubClass);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['MSSubClass'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd78e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3 第3列 MSZoning 地区分类 object类型 无空值;需将文字替换为数字，然后用哑编码，此处删除\n",
    "sns.countplot(data.MSZoning);\n",
    "plt.show()\n",
    "# 替换数字，将RL替换为1，RM替换为2，C替换为3，FV替换为4，RH替换为5\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['MSZoning'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>313.0</td>\n",
       "      <td>27650</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR2</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Mod</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>PosA</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1960</td>\n",
       "      <td>2007</td>\n",
       "      <td>Flat</td>\n",
       "      <td>Tar&amp;Grv</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>425</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>585</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2069</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2069</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>505</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>242000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>313.0</td>\n",
       "      <td>63887</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>Hip</td>\n",
       "      <td>ClyTile</td>\n",
       "      <td>Stucco</td>\n",
       "      <td>Stucco</td>\n",
       "      <td>Stone</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>5644</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>466</td>\n",
       "      <td>6110</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>4692</td>\n",
       "      <td>950</td>\n",
       "      <td>0</td>\n",
       "      <td>5642</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Ex</td>\n",
       "      <td>12</td>\n",
       "      <td>Typ</td>\n",
       "      <td>3</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2</td>\n",
       "      <td>1418</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>214</td>\n",
       "      <td>292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotFrontage  LotArea Street Alley LotShape LandContour Utilities  \\\n",
       "934         313.0    27650   Pave   NaN      IR2         HLS    AllPub   \n",
       "1298        313.0    63887   Pave   NaN      IR3         Bnk    AllPub   \n",
       "\n",
       "     LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType  \\\n",
       "934     Inside       Mod        NAmes       PosA       Norm     1Fam   \n",
       "1298    Corner       Gtl      Edwards      Feedr       Norm     1Fam   \n",
       "\n",
       "     HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle  \\\n",
       "934      1Story            7            7       1960          2007      Flat   \n",
       "1298     2Story           10            5       2008          2008       Hip   \n",
       "\n",
       "     RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual  \\\n",
       "934   Tar&Grv     Wd Sdng     Wd Sdng       None         0.0        TA   \n",
       "1298  ClyTile      Stucco      Stucco      Stone       796.0        Ex   \n",
       "\n",
       "     ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  \\\n",
       "934         TA     CBlock       Gd       TA           Gd          GLQ   \n",
       "1298        TA      PConc       Ex       TA           Gd          GLQ   \n",
       "\n",
       "      BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating  \\\n",
       "934          425          Unf           0        160          585    GasA   \n",
       "1298        5644          Unf           0        466         6110    GasA   \n",
       "\n",
       "     HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "934         Ex          Y      SBrkr      2069         0             0   \n",
       "1298        Ex          Y      SBrkr      4692       950             0   \n",
       "\n",
       "      GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "934        2069             1             0         2         0             4   \n",
       "1298       5642             2             0         2         1             3   \n",
       "\n",
       "      KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces  \\\n",
       "934              1          Gd             9        Typ           1   \n",
       "1298             1          Ex            12        Typ           3   \n",
       "\n",
       "     FireplaceQu GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea  \\\n",
       "934           Gd     Attchd       1960.0          RFn           2         505   \n",
       "1298          Gd     Attchd       2008.0          Fin           2        1418   \n",
       "\n",
       "     GarageQual GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  \\\n",
       "934          TA         TA          Y           0            0              0   \n",
       "1298         TA         TA          Y         214          292              0   \n",
       "\n",
       "      3SsnPorch  ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  \\\n",
       "934           0            0         0    NaN   NaN         NaN        0   \n",
       "1298          0            0       480     Gd   NaN         NaN        0   \n",
       "\n",
       "      MoSold  YrSold SaleType SaleCondition  SalePrice  \n",
       "934       11    2008       WD        Normal     242000  \n",
       "1298       1    2008      New       Partial     160000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FmN3ktfeheDOby7tRoRXyGsIhkaI3xjxvjJkyxkwDX8Uyz4DVg7/EderFwHM+19hujOkz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\nOaroFUVRco4qekVRlJzz/wHdyVuZ7En4swAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd79aac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4 第4列 LotFrontage 房屋到街道的直线距离(英尺) float类型 有部分空值\n",
    "\n",
    "plt.scatter(range(data.shape[0]), data[\"LotFrontage\"].values,color='purple')\n",
    "plt.title(\"LotFrontage\");\n",
    "#散点图中有2个较为明显的离群点，在300附近，可删除\n",
    "data[data['LotFrontage']>200]\n",
    "\n",
    "#fig = plt.figure()\n",
    "#sns.distplot(data.LotFrontage.values, bins=10, kde=False)\n",
    "#plt.xlabel('LotFrontage',fontsize=12)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除大于200的离群点,删除前先处理空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1201.000000\n",
       "mean       70.049958\n",
       "std        24.284752\n",
       "min        21.000000\n",
       "25%        59.000000\n",
       "50%        69.000000\n",
       "75%        80.000000\n",
       "max       313.000000\n",
       "Name: LotFrontage, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"LotFrontage\"].describe() \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 平均数与中位数接近，同时数据分布较为集中，选中用均值70填充空值\n",
    "#data['LotFrontage'].fillna(70.0)\n",
    "values = {'LotFrontage': 70}\n",
    "data=data.fillna(value=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 删除LotFrontage大于200的样本\n",
    "#data = data[data.LotFrontage ]\n",
    "data = data[data['LotFrontage']<210] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IOag2ZJ1Vbm+187WqXGPWk+d3XE4r8lXPOvLYP6vRkG3LaZ9o9PqnxSBMZjYL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\npqAhIiLR/j9SscTPCGYuhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc864a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5 第5列 LotArea 地块面积  int64 无空值\n",
    "plt.scatter(range(data.shape[0]), data[\"LotArea\"].values,color='purple')\n",
    "plt.title(\"LotArea\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除大于100000的离群点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "data = data[data['LotArea']<100000] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下特征均不为数值型，删除这些列\n",
    "Street           1460 non-null object\n",
    "Alley            91 non-null object\n",
    "LotShape         1460 non-null object\n",
    "LandContour      1460 non-null object\n",
    "Utilities        1460 non-null object\n",
    "LotConfig        1460 non-null object\n",
    "LandSlope        1460 non-null object\n",
    "Neighborhood     1460 non-null object\n",
    "Condition1       1460 non-null object\n",
    "Condition2       1460 non-null object\n",
    "BldgType         1460 non-null object\n",
    "HouseStyle       1460 non-null object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['Street', 'Alley'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " data=data.drop(['LotShape','LandContour','Utilities','LotConfig','LandSlope','Neighborhood','Condition1','Condition2','BldgType','HouseStyle'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc30ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 18  第18行 OverallQual Int64 整体材质和完成品质 无空值\n",
    "sns.countplot(data.OverallQual);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc30c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 19  第19行 OverallCond 整体材质和完成品质 无空值\n",
    "sns.countplot(data.OverallCond);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe0ff400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 20  第20行 YearBuilt 建筑年代 无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.YearBuilt.values, bins=30, kde=False)\n",
    "plt.xlabel('YearBuilt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "修改建筑年代为建筑年龄，用2017减去YearBuilt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['YearBuilt']=2017-data['YearBuilt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe1fd128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 21  第21行 YearRemodAdd 重建年份 无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.YearRemodAdd.values, bins=30, kde=False)\n",
    "plt.xlabel('YearRemodAdd', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "修改重建年份为重建年龄，YearRemodAdd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['YearRemodAdd']=2017-data['YearRemodAdd']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下特征均不为数值型，删除这些列\n",
    "RoofStyle        1460 non-null object\n",
    "RoofMatl         1460 non-null object\n",
    "Exterior1st      1460 non-null object\n",
    "Exterior2nd      1460 non-null object\n",
    "MasVnrType       1452 non-null object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>70.0</td>\n",
       "      <td>7851</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>625</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>860</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>860</td>\n",
       "      <td>1100</td>\n",
       "      <td>0</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>BuiltIn</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2</td>\n",
       "      <td>440</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>288</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>216500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>70.0</td>\n",
       "      <td>32668</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>1219</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>816</td>\n",
       "      <td>2035</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2515</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>9</td>\n",
       "      <td>Maj1</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>484</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Alloca</td>\n",
       "      <td>200624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>65.0</td>\n",
       "      <td>8125</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>813</td>\n",
       "      <td>813</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>822</td>\n",
       "      <td>843</td>\n",
       "      <td>0</td>\n",
       "      <td>1665</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>562</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>205950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>67.0</td>\n",
       "      <td>10083</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>833</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>343</td>\n",
       "      <td>1176</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>184900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>95.0</td>\n",
       "      <td>11639</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>1428</td>\n",
       "      <td>1428</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1428</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1428</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2</td>\n",
       "      <td>480</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>182000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>35.0</td>\n",
       "      <td>4274</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1106</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>1241</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1241</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1241</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>4</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2</td>\n",
       "      <td>569</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>199900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>107.0</td>\n",
       "      <td>13891</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Gd</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1386</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>2076</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2076</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2076</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Ex</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>3</td>\n",
       "      <td>850</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>216</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>465000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>75.0</td>\n",
       "      <td>9473</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>804</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>1128</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1128</td>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>2031</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>577</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>237000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "234          70.0     7851            6            5         15            15   \n",
       "529          70.0    32668            6            3         60            42   \n",
       "650          65.0     8125            7            6         10            10   \n",
       "936          67.0    10083            7            5         14            14   \n",
       "973          95.0    11639            7            5         10             9   \n",
       "977          35.0     4274            7            5         11            10   \n",
       "1243        107.0    13891           10            5         11            11   \n",
       "1278         75.0     9473            8            5         15            15   \n",
       "\n",
       "      MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond  \\\n",
       "234          NaN        Gd        TA      PConc       Gd       TA   \n",
       "529          NaN        Gd        TA      PConc       TA       TA   \n",
       "650          NaN        Gd        TA      PConc       Gd       TA   \n",
       "936          NaN        Gd        TA      PConc       Gd       TA   \n",
       "973          NaN        Gd        TA      PConc       Gd       TA   \n",
       "977          NaN        Gd        TA      PConc       Gd       TA   \n",
       "1243         NaN        Ex        TA      PConc       Ex       Gd   \n",
       "1278         NaN        Gd        TA      PConc       Gd       TA   \n",
       "\n",
       "     BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  \\\n",
       "234            No          GLQ         625          Unf           0   \n",
       "529            No          Rec        1219          Unf           0   \n",
       "650            No          Unf           0          Unf           0   \n",
       "936            No          GLQ         833          Unf           0   \n",
       "973            No          Unf           0          Unf           0   \n",
       "977            No          GLQ        1106          Unf           0   \n",
       "1243           Gd          GLQ        1386          Unf           0   \n",
       "1278           No          GLQ         804          Unf           0   \n",
       "\n",
       "      BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  \\\n",
       "234         235          860    GasA        Ex          Y      SBrkr   \n",
       "529         816         2035    GasA        TA          Y      SBrkr   \n",
       "650         813          813    GasA        Ex          Y      SBrkr   \n",
       "936         343         1176    GasA        Ex          Y      SBrkr   \n",
       "973        1428         1428    GasA        Ex          Y      SBrkr   \n",
       "977         135         1241    GasA        Ex          Y      SBrkr   \n",
       "1243        690         2076    GasA        Ex          Y      SBrkr   \n",
       "1278        324         1128    GasA        Ex          Y      SBrkr   \n",
       "\n",
       "      1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  \\\n",
       "234        860      1100             0       1960             1             0   \n",
       "529       2515         0             0       2515             1             0   \n",
       "650        822       843             0       1665             0             0   \n",
       "936       1200         0             0       1200             1             0   \n",
       "973       1428         0             0       1428             0             0   \n",
       "977       1241         0             0       1241             1             0   \n",
       "1243      2076         0             0       2076             1             0   \n",
       "1278      1128       903             0       2031             1             0   \n",
       "\n",
       "      FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  \\\n",
       "234          2         1             4             1          Gd   \n",
       "529          3         0             4             2          TA   \n",
       "650          2         1             3             1          Gd   \n",
       "936          2         0             2             1          Gd   \n",
       "973          2         0             3             1          Gd   \n",
       "977          1         1             1             1          Gd   \n",
       "1243         2         1             2             1          Ex   \n",
       "1278         2         1             3             1          Gd   \n",
       "\n",
       "      TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  GarageYrBlt  \\\n",
       "234              8        Typ           2          TA    BuiltIn       2002.0   \n",
       "529              9       Maj1           2          TA     Attchd       1975.0   \n",
       "650              7        Typ           0         NaN     Attchd       2007.0   \n",
       "936              5        Typ           0         NaN     Attchd       2003.0   \n",
       "973              6        Typ           0         NaN     Attchd       2007.0   \n",
       "977              4        Typ           0         NaN     Attchd       2007.0   \n",
       "1243             7        Typ           1          Gd     Attchd       2006.0   \n",
       "1278             7        Typ           1          Gd     Attchd       2002.0   \n",
       "\n",
       "     GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  \\\n",
       "234           Fin           2         440         TA         TA          Y   \n",
       "529           RFn           2         484         TA         TA          Y   \n",
       "650           RFn           2         562         TA         TA          Y   \n",
       "936           RFn           2         555         TA         TA          Y   \n",
       "973           Fin           2         480         TA         TA          Y   \n",
       "977           Fin           2         569         TA         TA          Y   \n",
       "1243          Fin           3         850         TA         TA          Y   \n",
       "1278          RFn           2         577         TA         TA          Y   \n",
       "\n",
       "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "234          288           48              0          0            0   \n",
       "529            0            0            200          0            0   \n",
       "650            0            0              0          0            0   \n",
       "936            0           41              0          0            0   \n",
       "973            0          120              0          0            0   \n",
       "977            0          116              0          0            0   \n",
       "1243         216          229              0          0            0   \n",
       "1278           0          211              0          0            0   \n",
       "\n",
       "      PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold SaleType  \\\n",
       "234          0    NaN   NaN         NaN        0       5    2010       WD   \n",
       "529          0    NaN   NaN         NaN        0       3    2007       WD   \n",
       "650          0    NaN   NaN         NaN        0       5    2008       WD   \n",
       "936          0    NaN   NaN         NaN        0       8    2009       WD   \n",
       "973          0    NaN   NaN         NaN        0      12    2008      New   \n",
       "977          0    NaN   NaN         NaN        0      11    2007      New   \n",
       "1243         0    NaN   NaN         NaN        0       9    2006      New   \n",
       "1278         0    NaN   NaN         NaN        0       3    2008       WD   \n",
       "\n",
       "     SaleCondition  SalePrice  \n",
       "234         Normal     216500  \n",
       "529         Alloca     200624  \n",
       "650         Normal     205950  \n",
       "936         Normal     184900  \n",
       "973        Partial     182000  \n",
       "977        Partial     199900  \n",
       "1243       Partial     465000  \n",
       "1278        Normal     237000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 27  第27行 MasVnrArea  表层砌体面积 Float64 有少量空值\n",
    "data[data['MasVnrArea'].isnull().values==True ] \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除空值样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data[data['MasVnrArea'].isnull().values==False] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAENCAYAAAABh67pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL0md+/8o/9N8OEHuTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe629ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.MasVnrArea.values, bins=30, kde=False)\n",
    "plt.xlabel('MasVnrArea', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下特征均不为数值型，删除这些列\n",
    "ExterQual        1460 non-null object\n",
    "ExterCond        1460 non-null object\n",
    "Foundation       1460 non-null object\n",
    "BsmtQual         1423 non-null object\n",
    "BsmtCond         1423 non-null object\n",
    "BsmtExposure     1422 non-null object\n",
    "BsmtFinType1     1423 non-null object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下列不作为特征，删除这些列\n",
    "BsmtFinSF1       1460 non-null int64\n",
    "BsmtFinType2     1422 non-null object\n",
    "BsmtFinSF2       1460 non-null int64\n",
    "BsmtUnfSF        1460 non-null int64\n",
    "\n",
    "Heating          1460 non-null object\n",
    "HeatingQC        1460 non-null object\n",
    "CentralAir       1460 non-null object\n",
    "Electrical       1459 non-null object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'Heating', 'HeatingQC', 'CentralAir','Electrical'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5afef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    TotalBsmtSF 地下室总面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.TotalBsmtSF.values, bins=30, kde=False)\n",
    "plt.xlabel('TotalBsmtSF', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1stFlrSF         1460 non-null int64\n",
    "2ndFlrSF         1460 non-null int64\n",
    "LowQualFinSF     1460 non-null int64\n",
    "GrLivArea        1460 non-null int64\n",
    "BsmtFullBath     1460 non-null int64\n",
    "BsmtHalfBath     1460 non-null int64\n",
    "FullBath         1460 non-null int64\n",
    "HalfBath         1460 non-null int64\n",
    "BedroomAbvGr     1460 non-null int64\n",
    "KitchenAbvGr     1460 non-null int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vx7Xl49ZYn94O/OYidU8DbgOOBU4G7mZwsv+YtnwK8LRW57RV6s+JwOlt+ZnAf7V2r8v9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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe9e7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    1stFlrSF 第一层的面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['1stFlrSF'].values, bins=30, kde=False)\n",
    "plt.xlabel('1stFlrSF', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " data=data[data['1stFlrSF']<3000] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WuCWS5g36qnrDCXZdNUfdAm5cbKOk4/mHQzp1/jJWkjpn0EtS5wx6SeqcQS9JnZv3w1hp\n3NAPRSWdOQx6LSv/cEhLz6kbSeqcI3o5qpY654hekjrniL5jjtQlgUGvzniqBOmZnLqRpM45otdZ\nyZG/ziYGvXQSC/mcwz8KOlMZ9CuQH7KemfwvQWcqg146zfyDoNNtSYI+yeuA3wXOAd5fVe9eisfp\njSN1jZv068E/HGeviX/rJsk5wPuALcAVwBuSXDHpx5EkDbMUI/qXA4eq6kGAJB8BtgJfXoLHWlaO\nwCWtBEsR9GuAw2Pb08ArluBxJC3ApD8bWIqBzqSnl/w8ZGQpgj5zlNUzKiU7gB1t87tJvnqKj3cR\n8JfzVXrjKR68I4P6SfbTgPfKkvXRcr1Pl+hxT8dr6WeHVFqKoJ8G1o1trwWOHF+pqnYCOxf7YEn2\nVdWmxR6nd/bTMPbT/OyjYc6kflqKUyD8ObAhyeVJzgWuA3YvweNIkgaY+Ii+qp5K8mbgTxh9vfID\nVfXApB9HkjTMknyPvqruAu5aimPPYdHTP2cJ+2kY+2l+9tEwZ0w/peoZn5NKkjriaYolqXMrOuiT\nvC7JV5McSnLTcrdnOSV5KMmXktyXZF8ruyDJ3UkOtuX5rTxJbmn9tj/JxuVt/dJJ8oEkx5LcP1a2\n4H5Jsq3VP5hk23I8l6V0gn56V5K/aK+p+5JcPbbvt1o/fTXJL4+Vd/ueTLIuyWeSHEjyQJK3tPIz\n//VUVSvyxuiD3q8BLwDOBb4IXLHc7VrG/ngIuOi4sv8M3NTWbwLe09avBv4no988bAb2Lnf7l7Bf\nXg1sBO4/1X4BLgAebMvz2/r5y/3cTkM/vQv4N3PUvaK9384DLm/vw3N6f08ClwIb2/rzgP/b+uKM\nfz2t5BH9/z/VQlX9EJg91YKethXY1dZ3AdeMld9eI/cAq5NcuhwNXGpV9afAo8cVL7Rffhm4u6oe\nrarHgLuB1y1960+fE/TTiWwFPlJVP6iqrwOHGL0fu35PVtXRqvp8W/8OcIDRmQDO+NfTSg76uU61\nsGaZ2nImKOBTSe5tvzoGuKSqjsLoRQpc3MrP9r5baL+czf315jbt8IHZKQnsJ5KsB14G7GUFvJ5W\nctAPOtXCWeSVVbWR0VlDb0zy6pPUte/mdqJ+OVv761bgbwNXAkeB/9rKz+p+SvJc4I+At1bVt09W\ndY6yZemnlRz0g061cLaoqiNteQz4BKN/ox+ZnZJpy2Ot+tnedwvtl7Oyv6rqkar6cVX9BPg9Rq8p\nOIv7KcmzGIX8B6vq4634jH89reSg91QLTZLnJHne7DrwWuB+Rv0x+4n+NuDOtr4buL59K2Az8MTs\nv55niYX2y58Ar01yfpu+eG0r69pxn9v8M0avKRj103VJzktyObAB+BydvyeTBLgNOFBVvzO268x/\nPS33J9mL/BT8akaffH8N+O3lbs8y9sMLGH3D4YvAA7N9AVwI7AEOtuUFrTyMLg7zNeBLwKblfg5L\n2DcfZjTt8CNGI6ntp9IvwG8w+tDxEHDDcj+v09RPf9j6YT+j0Lp0rP5vt376KrBlrLzb9yTwKkZT\nLPuB+9rt6pXwevKXsZLUuZU8dSNJGsCgl6TOGfSS1DmDXpI6Z9BLUucMeglI8qYkfzag3i8kmT4d\nbZImxaDXitV+sHNbkm8k+U6SLyTZMqFjV5LvJfluuz2+gPu+Ksn/TvJEkkeT/K8kf6/te1OSH48d\n97tJ/tsk2iydyJJcSlA6TVYxOjnUPwIeZvTjlTuS/N2qemgCx39pVR0aWjnJKuDZwCeBfwHcweh0\nvf8Q+MFY1f9TVa+aQPukQRzRa8Wqqu9V1buq6qGq+klVfRL4OvDzs1MsSd7eLqhxNMkNs/dNcmGS\n3Um+neRzjE7etWAZXfDlHUn2A98D/k5r24drdJ6Yv6qqT1XV/gk8ZemUGPTqRpJLGAXtA63obwLP\nZ3QK2O3A+8ZOtfs+4PuMLibxG+12qt4A/AqwmtHP/3+cZFeSLWOPJy0bg15daGcV/CCwq6q+0op/\nBPzHqvpRVd0FfBd4UZJzgF8D/n37r+B+nr5wxLjPJ3m83W45ycPfUlWH2+j92zx9TpTfA2bafw6X\njNXfPHbcx9sJr6Ql4xy9VrwkP8XoBFw/BN48tutbVfXU2PaTwHOBKZ6e35/1jTkOvXHgHP34caiq\nA8CbWtteDPx34L2MRv4A9zhHr9PJEb1WtLFTx14C/FpV/WjA3WaAp/jr5wS/bBHNOOGZAdt/F38A\n/Nwiji8tikGvle5W4CXAP62qvxpyh6r6MfBx4F1Jnp3kCp4+n/iiJHlx+wB4bdtex2gkf88kji+d\nCoNeK1aSnwV+k9Gl7r459r30Nw64+5sZTeN8k9GI+/cn1KzvAK8A9ib5HqOAvx94+4SOLy2Y56OX\npM45opekzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXMGvSR1zqCXpM79P3DpWfKu/iLrAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xedaa6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    2ndFlrSF 第二层的面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['2ndFlrSF'].values, bins=30, kde=False)\n",
    "plt.xlabel('2ndFlrSF', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WuCWS5g36qnrDCXZdNUfdAm5cbKOk4/mHQzp1/jJWkjpn0EtS5wx6SeqcQS9JnZv3w1hp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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebd65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    LowQualFinSF  低质量完成的面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['2ndFlrSF'].values, bins=30, kde=False)\n",
    "plt.xlabel('2ndFlrSF', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1bf45wGeBY4DTgF3AiqX+fBNuq7cAm4C/bMu2UZVn9NNSVTcD9x9QfB5wdZu/Gnj1UPkH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\nS1LnDHpJ6pxBL0mdM+glqXP/H+sh2Sspvy8ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe0e7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    GrLivArea  地上居住面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['GrLivArea'].values, bins=30, kde=False)\n",
    "plt.xlabel('GrLivArea', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sBh4FLhmwNknSAgYLiKr6c/rnFQDO6/Qv4NKh6pEkHRx/SS1J6jIgJEldBoQkqcuAkCR1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\nMiAkSV0GhCSpy4CQJHX9f716x5Ntn+72AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xecf08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    BsmtFullBath  地下室全浴室数目    int64  无空值\n",
    "sns.countplot(data.BsmtFullBath);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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64MAkhy9y25KkZqL3OJKsAV4C3AAcVlUPwiBcgEPbsJXA/UMvm2m1Hfe1PsnmJJtnZ2fH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\nYXVP5tksAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xecfe588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    BsmtHalfBath  地下室半浴室数目    int64  无空值\n",
    "sns.countplot(data.BsmtHalfBath);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xeace828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    FullBath  地上全浴室数    int64  无空值\n",
    "sns.countplot(data.FullBath);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Dfgz4Mw2Y3jwPqc4Fti9YPwnk2xr3zewofW9A3hwO/6iNvSoJH/Vxv1T57zSoPygnAQk\n+TFwzYKuE4DNVfXaJMcDd1ZVJfk94IlV9aYkrwRWtzE/2W7n+zpwN/OLLz4GeElVfartO6Gq7mj/\n4H8J+JWq+naSe6rqYW3MLLCznXN7kotbPX8z8B+F9BPLxi5AmhL/0y4HAfP3F4DVrbkC+Fj7Mpmj\nga9NeM5nV9XtSR4LXJ7kc1V1D/D6JL/ZxqwEVgHf7hz/tara3ra3AbMH8huSflZeYpIWdz7wvqp6\nCvBq4JgDObiq/hu4FXhSkl8Fngs8o6qeCly1n/P9YMH2j/E/dDrEDAhpcY8Avtm21x3owUkeCZwC\nfKOda3dVfS/JE4DTFwz9UVtqWpoKBoS0uLcBf5fk88DtB3DcFUm2A1cA51bVrcCngWVJrgbeDnxx\nwfiNwNULblJLo/ImtSSpyxmEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV3/B+9s\nrs2NspgHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbb87c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    HalfBath  地上半浴室数目    int64  无空值\n",
    "sns.countplot(data.HalfBath);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd7ac908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    BedroomAbvGr  地下室之上的卧室数目    int64  无空值\n",
    "sns.countplot(data.BedroomAbvGr);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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euKgGbgAWJzlgjtuWJDW9XuNIMg68FrgR2L+qHoFBuAAvasOWAg8NPW2i1SRJPegtOJI8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\nDklSJwaHJKkTg0OS1InBIUnq5P8B5gbbIiHHjkAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe342d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    KitchenAbvGr  厨房数目    int64  无空值\n",
    "sns.countplot(data.KitchenAbvGr);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除以下列\n",
    "KitchenQual      1460 non-null object\n",
    "Functional       1460 non-null object\n",
    "FireplaceQu      770 non-null object\n",
    "GarageType       1379 non-null object\n",
    "GarageFinish     1379 non-null object\n",
    "GarageQual       1379 non-null object\n",
    "GarageCond       1379 non-null object\n",
    "PavedDrive       1460 non-null object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TotRmsAbvGrd     1460 non-null int64\n",
    "Fireplaces       1460 non-null int64\n",
    "GarageYrBlt      1379 non-null float64\n",
    "GarageCars       1460 non-null int64\n",
    "GarageArea       1460 non-null int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe10f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    TotRmsAbvGrd  地上房间总数    int64  无空值\n",
    "sns.countplot(data.TotRmsAbvGrd);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MST4DvAU4PskM8CHgcuDqJBuAh4ELWvcbgPOAXcAzwMVD1SVJGs9gAVFV73meVed0+hZw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\nASFJ6jIgJEldBoQkqev/Aq6fMzyTehLcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc2a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    Fireplaces  壁炉的数目    int64  无空值\n",
    "sns.countplot(data.Fireplaces);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#    GarageYrBlt  车库建造年份    int64  删除\n",
    "data=data.drop(['GarageYrBlt'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k7cFgAVFVn6N/XgFgRWd8AecOVY8k6eEZcg9C0hz2mV973qRL2O+e99nPTLqEg4qP2pAk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\nJHUZEJKkLgNCktRlQEiSugwISVLX/wecxAYlfwsQUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe1020f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    GarageCars  车库大小(能停的车辆数目)    int64  无空值\n",
    "sns.countplot(data.GarageCars);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#    GarageArea  与GarageCars冗余   int64  删除\n",
    "data=data.drop(['GarageArea'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WoodDeckSF       1460 non-null int64\n",
    "OpenPorchSF      1460 non-null int64\n",
    "EnclosedPorch    1460 non-null int64\n",
    "3SsnPorch        1460 non-null int64\n",
    "ScreenPorch      1460 non-null int64\n",
    "PoolArea         1460 non-null int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DfbIkO6tq3eGup0dum4Nz2xyc2+bgluq2WYijbj4PrE3ylCSPBM4Hti3A80iSxjDve/RV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\nAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdebf7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    WoodDeckSF  木头 deck 面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['WoodDeckSF'].values, bins=30, kde=False)\n",
    "plt.xlabel('WoodDeckSF', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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An82vjFNpKYJ+Ctg4sr0B2H9wpaq6FLh0oS+WZLKqtiz0OCuV/Vu9eu4b2L/VZCmuuvk8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\nktS5/w/tfb6JD77WqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc07cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    OpenPorchSF  开放门廊面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['OpenPorchSF'].values, bins=30, kde=False)\n",
    "plt.xlabel('OpenPorchSF', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xeaf4908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    EnclosedPorch  封闭门廊面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['EnclosedPorch'].values, bins=30, kde=False)\n",
    "plt.xlabel('EnclosedPorch', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4f2R+Cjhpkfug/fD+W+5bku3+xknPWpLtSj1Z7ADILLV6TINkE7CpzX4vyd1jbO8o4Ntz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\n/x96PLX0dqYq1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed1a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    3SsnPorch  三季门廊面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['3SsnPorch'].values, bins=30, kde=False)\n",
    "plt.xlabel('3SsnPorch', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该列数值同一化严重，删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['3SsnPorch'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yGBcCZwOXJTl7bns1I24GLphU2wzsqKo1wI42D93PYk17bAKun6U+zpQDwNVV9WrgfODK\n9m+8FMb/18Bbquq1wOuAC5KcD3wUuK6N/RlgY2u/EXimql4BXNfaLXRXAbt65pfS2AF+rape1/Px\n0sG976tqUT2ANwBf6pm/Brhmrvs1Q2NdDTzQM/8IcEabPgN4pE3/F+CyqdothgdwO931sZbU+IEX\nAl+n+7b/U8BQq//0/wDdJ/re0KaHWrvMdd/7GPPK9kvvLcAX6b7suiTG3sbxHeDUSbWBve8X3R4C\nU18eY8Uc9WW2nV5VTwC059NafdH+TNphgNcDO1ki42+HTO4D9gHbgW8D+6vqQGvSO76fjr0tfxY4\nZXZ7PFAfA34X+EmbP4WlM3boruzw5ST3tqs6wADf97P+sdNZcMTLYyxBi/JnkuRE4HPA+6rqe8lU\nw+yaTlFbsOOvqueB1yVZDnwBePVUzdrzohl7kncA+6rq3iRvnihP0XTRjb3HG6tqb5LTgO1JHj5M\n22Me/2LcQzji5TEWsSeTnAHQnve1+qL7mST5RbowuKWqPt/KS2b8AFW1H/gK3XmU5Ukm/sDrHd9P\nx96WvwR4enZ7OjBvBN6Z5DvAp+kOG32MpTF2AKpqb3veR/fHwLkM8H2/GANhKV8eYxuwoU1voDu2\nPlG/on3q4Hzg2YldzIUo3a7AjcCuqvqDnkWLfvxJhtueAUleALyV7gTrXcC7WrPJY5/4mbwLuLPa\nAeWFpqquqaqVVbWa7v/1nVV1OUtg7ABJXpTklyamgbcBDzDI9/1cnySZoRMvFwHfoju2+sG57s8M\njfFW4Angx3R/CWykOz66A3i0PZ/c2obuk1ffBr4JrJ3r/vc59jfR7freD9zXHhcthfEDvwx8o439\nAeD3Wv3lwD3AKPDHwPGtfkKbH23LXz7XYxjQz+HNwBeX0tjbOP+sPR6c+N02yPe931SWJAGL85CR\nJGkaDARJEmAgSJIaA0GSBBgIkqTGQJAWmCQ3J/k3c90PLT4GghaMJG9K8n+TPJvk6ST/J8nfmet+\nTUjyoSQ/TvJXSfa3vr5hrvslHS0DQQtCkhfTXd3yPwAn012k68N0l4M+2teYjWt3faaqTgSGgT8F\nPp/DXGRpKrPUT+kgBoIWir8FUFW3VtXzVfX/qurLVXU/QJJ/ku6GOd9P8tDEzUDaDUU+kOR+4AdJ\nhpKcmeRzScaTPJ7kvRMbSfILSTYn+XaSv0xyW5KT27LVSSrJhiS7kzyV5INTdbaqfgxsBf4GcEp7\n3X+Z5Lvpbmz0ySQvmfS6G5PsBu5s9Yk9ov1J9iR5d88mTkryP9t4dyb5mwP+eWsJMhC0UHwLeD7J\n1iQXpt0VCiDJJcCHgCuAFwPvBP6yZ93LgF8HltNdNvl/0H39fwWwDnhfkre3tu8FLgZ+FTiT7oYr\nn5jUlzcBr2zr/l6Sg642muR44N3AWFU91abfDfwa3SUITgT+46TVfpXuyqVvT/JS4E/o9oiG6W6G\nc9+kMX0YOInu0gwfmdwH6ZjN9fU5fPg42gfdL8ub6a7ddIDu4l2n090I5apDrPMd4Dd75s8Ddk9q\ncw3wR216F7CuZ9kZdNeLGqK7IVEBK3uW3wNc2qY/BPwI2E93xck7gV9py3YA/6xnvVdO8bovn9Sn\nLxxiTDcDN/TMXwQ8PNf/Pj4W/sNjlVowqmoX3V/ZJHkV8N/oLn+8iu4CXofSe5OQlwFnJtnfU1sG\n/O+e5V9I8pOe5c/TBc+Ev+iZfo7ur/0Jt1XVP5qiD2cC3+2Z/y5dGPS+bm8/jzSmw/VBmhYPGWlB\nqqqH6f5Sfg3dL9LDHUPvvYLjHuDxqlre8/ilqrqoZ/mFk5afUFV/3meX99KFzYSX0u3lPHmYfnpe\nQLPKQNCCkORVSa5OsrLNr6I7jn43cAPwz5P8Srv2+yuSvOwQL3UP8L12ovkF6W5H+Zqej6/+Z+Aj\nE+u3+w+sH8AQbgV+p92n40Tg39J9IunAIdrfArw1yT9sJ8JPSfK6AfRDOiQDQQvF9+mO/+9M8gO6\nIHgAuLqq/pjupOqnWrv/TvfR1INUd/vJf0B3kvZxuhuv30B3Ny2AP6Q7N/HlJN9v2zlvAP2/Cfiv\nwFfbdn8I/PahGlfVbrpzA1fT3eXrPuC1A+iHdEjeD0GSBLiHIElqDARJEmAgSJIaA0GSBBgIkqTG\nQJAkAQaCJKkxECRJgIEgSWr+P/4/KZimreiWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3559e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    ScreenPorch  观景门廊    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['ScreenPorch'].values, bins=30, kde=False)\n",
    "plt.xlabel('ScreenPorch', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe8ac438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    PoolArea  游泳池面积    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['PoolArea'].values, bins=30, kde=False)\n",
    "plt.xlabel('PoolArea', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该列数值同一化严重，删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['PoolArea'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除非数值列\n",
    "PoolQC           7 non-null object\n",
    "Fence            281 non-null object\n",
    "MiscFeature      54 non-null object\n",
    "SaleType         1460 non-null object\n",
    "SaleCondition    1460 non-null object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['PoolQC','Fence','MiscFeature','SaleType','SaleCondition'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe40b3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    MiscVal  杂项功能价值    int64  无空值\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['MiscVal'].values, bins=30, kde=False)\n",
    "plt.xlabel('MiscVal', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该列数值同一化严重，删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['MiscVal'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-63-9d69b75df095>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-63-9d69b75df095>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    MiscVal          1460 non-null int64\u001b[0m\n\u001b[1;37m                        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "MiscVal          1460 non-null int64\n",
    "MoSold           1460 non-null int64\n",
    "YrSold           1460 non-null int64\n",
    "SalePrice        1460 non-null int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S5L7hed/O9c4cPt9Pkizo2UU7qPfB4edzbZIbkrx0IWvuVgEBXAGcMsF624B3VtUrgeOB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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb3f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    MoSold  销售月份    int64  无空值\n",
    "sns.countplot(data.MoSold);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe42b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    YrSold  销售年份    int64  无空值\n",
    "sns.countplot(data.YrSold);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1444 entries, 0 to 1459\n",
      "Data columns (total 28 columns):\n",
      "LotFrontage      1444 non-null float64\n",
      "LotArea          1444 non-null int64\n",
      "OverallQual      1444 non-null int64\n",
      "OverallCond      1444 non-null int64\n",
      "YearBuilt        1444 non-null int64\n",
      "YearRemodAdd     1444 non-null int64\n",
      "MasVnrArea       1444 non-null float64\n",
      "TotalBsmtSF      1444 non-null int64\n",
      "1stFlrSF         1444 non-null int64\n",
      "2ndFlrSF         1444 non-null int64\n",
      "LowQualFinSF     1444 non-null int64\n",
      "GrLivArea        1444 non-null int64\n",
      "BsmtFullBath     1444 non-null int64\n",
      "BsmtHalfBath     1444 non-null int64\n",
      "FullBath         1444 non-null int64\n",
      "HalfBath         1444 non-null int64\n",
      "BedroomAbvGr     1444 non-null int64\n",
      "KitchenAbvGr     1444 non-null int64\n",
      "TotRmsAbvGrd     1444 non-null int64\n",
      "Fireplaces       1444 non-null int64\n",
      "GarageCars       1444 non-null int64\n",
      "WoodDeckSF       1444 non-null int64\n",
      "OpenPorchSF      1444 non-null int64\n",
      "EnclosedPorch    1444 non-null int64\n",
      "ScreenPorch      1444 non-null int64\n",
      "MoSold           1444 non-null int64\n",
      "YrSold           1444 non-null int64\n",
      "SalePrice        1444 non-null int64\n",
      "dtypes: float64(2), int64(26)\n",
      "memory usage: 367.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oVEWzTndqaipdbfvTooE9jRrXp2btGnrP/sJsuXyNMuVKM+/XGfzw7U9vdsyX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swo1rQUQav0gc8T5B63bN0z07QKGqJYgJCuehsR5f23kSp2T1ODZRvbCwzoKaSgUo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EEQBEEQBEEsHfMaxIx2QRAEQRAEQRAEQRAEQRAEQXgNYqBdEARBEARBEARBEARBEARBEF6D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\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tZbH/r8z9bz7VPZwBqNO0Lt9sX8RPgb+x2P9XugztWqSsuWjfsx2rDq5gTchKXn7/pSLn\nW3RswR9+ywiI9KP7kG6G415dWvHn3uWGH7+bu3l+QBezaOzTtxsnzu7ldOg+PpxeXJtmxYpVv3A6\ndB+BQVsMbVoeHp5uRKtDmTI1v+7aV7Vj1drfOH7Gn+On/WnfwavMOvv378mlS4e4eiWETz55v1id\n69Yt5+qVEI6E7KJ2bU/DuZkzp3D1SgiXLh2iX78ehuM3wo5z7uw+Tp8K4PgxX8PxUaOGEhoaRNaD\nGNq2aVlm7aXh84U/0X3Iy4wY++5Tve/D6NSzAxsPr2HzkXWMm/JqkfNeHVuyeu9fhETvp9eQHkbn\njsTsZ03g36wJ/JsfVn1jVp1P2g6PGD0Y3+BNhp+IpFCaNm8EwNARA/A/tIXAI9uY/eU0k2vu2687\nZ87tI/RCENM+Lvp/bmVlxcrVSwi9EERQ8DZq6Z+/tm1bEnJsNyHHdnPk+B6GevcHwNraigMHt3Pk\n+B5OnPLnszkfmVxzHh17tmf9odVsDFnL2PdfKXK+VceW/OP/JwejAuk5xNh/OBQdyKqAv1gV8BeL\nVn5tNo0Affp25+TZAM6c389H098pct7KyooVq3/lzPn9BB4o2sZ5eroRE3/e0MZZW1uxL3grh4/t\n4ugpP2bN+dCkenv26cqhk7sJOePH+x+9WYxeS5avWEzIGT92Ba431GOAJs2eY+fedQQd9WHfke1Y\nW1sBMOyFgQSGbCPoqA9zvvrYpHoLUxF9uIo2furQsz1rDq5kXchqXn3/5SLnW3ZswV9+y9kfuZce\nhXyLv/f+YfgJuOlLVzP5FlAxx9W9+3Tj2Gl/Tp4LYGoJmv+38mdOngvAf/8mI83R8ec5cHgHBw7v\n4IcC47yNW//mQIgPh4/v5oefv0KhMN00UL9+PTgXup8LF4P5+OPJxei1YvWapVy4GEzwwR3UqqXz\njXr37krIkV2cPOlPyJFd9Oih8+erVKnMseO+hp+o6LN8//1ck+k1h+aCbNr8P06d2mtSvWD6drly\nFVsCDm01/Fy8GcJXC2eZXDdUzPGTAGStVOF/yguTbYYqSZIGuAhIgAaYIsvy0TJe0wtwl2XZV/95\nAvADEKc3uSDL8usPKd8TmCHL8lB92XayLE+RJGke8BaQBFQCDgDvy7Ksfci1RgBhsixf0X8O1l/7\ndFn+xsIoFAq++3EuY0a8gSougb0HNrPXN4iw6+EGm1dfH016eiadWg9gxKjBfPHVx7w9cToAURHR\n9On2gtE17965a3Qs4OBW9uwKNKVsI/2zvv2YyWM+IkGdyDr/vzkYEMKtsEiDjTougS8//IbX3zMe\nECUlpDDB+11ysnOwsbVhy8G1HNwbQlJCslm0Fqf9y+8+ZeKL7xOvSmBrwBr2+x8iPCwiX3tsPLM+\nmMek94o67U8LhULBoh+/ZPTwiaji4gkM3oq/736jOvLa6y+Snp5BB69+vDBqCF9+9QlvTtQNdCMj\nounVdfhT1/z+1+8z+9XPSFYn89vuXzkeeILoG9EGm6S4RH6c/iOj3zHufDv0bk+D5vWZPOB9LK0s\nWbzle04dOM29O/fMqvezbz/m7TEfkqBOZL3/PwQHHC5Uj+P5/MMFTHjvNaOyD+4/YM4H84mOiMXZ\npTobAlZy9MAJbmfeMZvePM0Lvv+M10a9TbwqgZ371rPPP5gb128ZbF4aO5KM9Ex6tB+K9wsDmfXl\nR0x5cyZDhvfHysqSAd1GUcmmEvuObmfnVj9iY1Rm0yspJEbOf4M/xn5DRnwK03Yu5HLgGRJuxhls\n4q5E8rP3Z+Q8yKbL2H4Mnf0aa6f8SpNerfFoVocfB3+KhZUl72+cy9XgULLu3DebXtB9x5MWvMOC\n174kNT6Fb3cu5vS+k8TeiDHYRFyO4NOh08l+kE3/sQMZN3sCP0/5gaz7Wfw27RfiI9VUq+HIoj0/\nEnroHPcy75pd89SvpzDz1VkkqZP5fc9vHAs4RlSBZy8xLpHvpy/mxXdGG5UNPXqedwboBh92Dnas\nCVnJ6YNnzKLx+x/nMXL4BFRx8ew/uBX/PUFcv37TYDNW3++18+rLyFFDmDf/EyZNyJ+8W/jdHPYH\nHjK67rfff87+fYeYMO4DLC0tsbGtVGadS379hkGDXyE2Vs3xY77s3h3A1as3DDZvTHyF9LQMmjTt\nypgxw1i4jcYGoQAAIABJREFUcA6vvTaZJk0a8tKY4bTy6o27uwv+fhto2qwbWq3O7ejb70VSUtKM\n7nf58jXGjHmL35d9VybdT8KIwf14ddQwPluw+KnfuzgUCgUzFn7I1JdnkKhOYqXvHxzee4TIG1EG\nm4S4RBZ89B2vvlv0ZVLWg2xe71d0UGoOnU/aDu/Y4suOLboXLY2aNOTvf3/lyqXrOFSrymdfTWdo\n75dJTUnjx2Vf83z3jhw5dMJkmn/86SuGe79OXFw8wYd34LtnH9ev5T9/r48fQ3p6Jl4tezNq9FC+\nWvApE8dP5cqVMHp0HY5Go8HF1Zmjx/fg57ufrKxshg5+jbt372FhYUHAvk0EBgRz6lSoSTQX1P7x\nNx/y0SufkKhO4m/f5YQEHC1ULxL4ZtoiXnl3TJHyWQ+ymdC/6Is9U6NQKPjhp3m8MGw8qrh4gg5t\nw893v9F3PG78i2SkZ9C2VR9Gjh7CvAUzmTQ+f/L8m0Vz2FegjcvKymb4kHGG79gvcAP7Ag5y2gTf\nsUKh4Jsf5vDKC2+hViXgG7SRAL8D3CjgZ74ybhQZGZl0bTuIYSMHMWfedCZPmoFSqWTJn9/x4buz\nuXLpOtWqVSUnJ5dq1ary+fwZDOz5Iqkpafzy+0K6du9IiInqcWH9FdGHq0jjJ4VCwYdff8CMVz8l\nSZ3EH3uWcSTgaBHf4rvp3/PSO8bPXujR87w5QPdCz87BjnUhqzllBt8iT2dFG1fnaX5xxERUcQkE\nHNiCfyHNunFeJh1a92fEqMHM/WoGb03UvYSNjIimV7eiL1smTfiQO7d1PufKtUsY9sJAdmz1LWL3\nJHp/+nk+3kPHEhcXz+HDO9mzJ5BrBdq38RPGkJ6eQcsWPRk92psFX89i/OtTSElJY/ToScSrE2na\n9Dl8dq6hYYNO3Llzl86dBhvKhxzZhY+Pf5m1mlNzHsOGD+CuGcao5miXs7Ky6d89f9ztd2ATvrtN\nP0dUEcdPAkFZMWVE+31Zlr1kWW4FzAa+NcE1vYDBhY5t1N/H62GT7I/Bz7IsewFNgRZAj0fYj9Db\nmpU2bVsScSuaqMhYcnJy2LHNl4FDjKMpBg7uw6b/dgCwa8deuhbzJrUk6tarTfXqjhw/atL3Awaa\nt25CTEQscdEqcnNy2btjPz0HdDOyUcfEc+NqOFqt8TbGuTm55GTnAGBlbYkkPd03UC3bNCMqMoaY\nqDhycnLZsyOAvoOMq0VcjJrrV26iLfmdjNlp064lEbeiiIqMIScnh+1b9zBoSF8jm0FD+rBh/XYA\ndu7wp1vPpxfxWxyNvJ5DFakiPjqe3JxcgncepHP/TkY2CbGJRFyLRFtoe+taDWtx4cRFtBotWfez\nuHUlgnY925pVb/PWTYkuUI/9d+yj1wDjSDiVoR4b14WoWzFER8QCkJSQTGpyGtWcHMyqF8CrTXMi\nI6IN9XfXdn/6DeplZNNvUE+2btgJgO/OQJ7v3hEAWZaxtbVFqVRSqZI1Odk53L5t3kFlLa8GJEfF\nkxqTiCZHw7ldR2nev52Rzc1jV8h5kA1A1LkbOLg6AuDa0IPwE1fRarRk389CdTWaxj1amVUvQAOv\nhsRHxpMYk0BuTi5Hdh2mXb8ORjaXj10kW6857Nx1HN2cAFBHqIiPVAOQlphKRnIG9o72Ztfc2KsR\ncZEq1Ppn74DPQbr0N44cS4hN4NbVCGRtyVvLdx/SjZMHTpP1IMvkGtsWatO2bd3DoKHG/d7gIX3Z\n8N82AHx2+NO9QJs2eGhfIiNjuFZgwtvOrgpdurRn7erNAOTk5JCZcbtMOju0b014eCQREdHk5OSw\ncZMP3t4DjGy8vfuzdq3unlu37qF3r6764wPYuMmH7OxsIiNjCA+PpEP71g+937VrNwkLC3+ojblo\n59WCqvZ25XLv4mjaujGxkXGootXk5uQS6BNE9wHPG9moY+O5efXWQ+uxuSlLO1yQYaMGsXObHwC1\n6ngSER5Fqv5FTMjB4wzy7lukzJPSrl0rbt2KIlL//G3dspshQ/sZ2QwZ2pf167YCsGO7Hz176tqQ\n+/cfoNFoAKhkbU3B7vvuXd1Eg6WlBRaWFsiy6f9fmhSqF/t9guhWKDI2PjaB8Ku3kLXl57e11X/H\nhjZuyx4GF/Hb+rJ+nc5v89nuT49CbVxUhHEbB8bfsaWlpcm+49ZtWxB5K4boKN1YxGebLwMGG9fj\n/oN6s3m9DwB7fALo2kPn0/Xo3YWrl8O4cuk6AGlpGWi1WmrVqcmtm5GGenz44DEGD+tvEr2FqYg+\nXEUbP+X7FrpnL8gnmOf7G7fJ8QbfouRnr8eQ7pw4cMosvgVUzHF1m7YtibwVVUDzHgYV0jxocG82\n/rfdoLnbY2jOm2S3sNC1F5iovWjXzotb4fl9yJYtuxg61PjZHjqkP+v+1fUh27f7GvqQ8+cvE69O\nBODKlTCsra2xsrIyKlu/fh2cnZ04cuSkSfSaU3PlyrZ88MGbLFr0m8m05mGOdrkgdevVorqzIyeO\nmv6lV0UcPwkEZcVcqWPsgTQASZLcJEk6JElSqCRJlyRJ6qY/fkeSpEWSJJ2RJGmfJEkdJEkKliTp\nliRJwyRJsgLmAy/pyxYNUdKjL9dO/3t1SZIiS6HVCl1Ue57etyRJOiVJ0nlJkrZKkmQrSVIXYBjw\ng15LfX3ZFyVJOilJUlje31VWXN1dUMWpDZ9VcfG4urkY2bi51SBOb6PRaLideRtHR50TWKu2J/sO\nb2P7nrV07Fx0MvKF0UPw2e5nCqnFUsPNmQRVouFzgjoRZzfnxy7v4l6DjUGr8TuznVXL1j21aHYA\nF7caxMclGD7HqxJxcavx1O7/uLi5uaCKjTd8VqnicXMvXEdciIvNryOZmbdxdKwG6OpI0OEd7PT9\nl06djSc2zYWTa3WSVEmGz8nqZKq7Oj1W2VtXI2jfsx3Wlayxr2ZPq84tcXZ//Dr1JLgUU49rlKIe\n59G8dVMsLS2JiYx7tHEZcXVzQV2g/qpVCbgWqr+ubi6oVDobXdtxh2qODvjuDOTevXucurKfY+cD\n+GvZajLSM82qt6qLI+mqFMPndHUqVV0cS7TvOKYXV4N1kXpxV6Np0tMLy0pWVK5mR4POTXFwe7z6\nVBYcXZ1IUee3SanqFJweUo/7vNSPc8FFHdYGrRpiYWVBQlR8MaVMS3W36iSp85+9pPgkqj/Bd9Vr\nWE8O7DhgSmkG3NxcDX0a6Po9t8L9nrsLcfp2T6PRkJlxB0enatja2vDhtLf5/lvjQU3tOjVJTk5l\n6R+LCA7x4del32Bra1Mmne4ersTG5q/yiItT4+HuWsQmRm+j0WjIyMjEyakaHu5Fy7p76MrKsoyf\n73pOHPfjzUnG0ZUCHc6uziQW6EMS1Uml8i2srK1Y6fcnf+/6ne4DzbfsuCztcEG8RwzAZ6vOV4u8\nFU39hnXxrOmOUqlkwODeuBWqd2XBzd2V2NiCz58a92Kev9jCPoWTzqdo164VJ075c+ykHx9N/dww\n8a5QKAg5tpvwyFMcCDrC6dPnTaY5D2fX6iQW6KsT1ck4u5auXqzwXc5fu5bSrdCLG1Oia78KtXGF\n/DZ390J+m1Eb9w6Lvi06caNQKDh0dCdhEScIDgrhjIm+Y1c347GIrh4b63V1r4EqrkCbnHmbao4O\n1KtfB2SZdVv+wj94M5On6tInRd6KpoFRPe5jaANNTUX04Sra+MnZrTpJ6ny9SfFJOD+Bb9F7WE+C\ndgSZUpoRFXFc7ebuQlxcgXFeXEIRn8jVzcVIc9Fx3nZ89qylUyHNm7b9zdXwo9y5c5edO0yT2sTd\n3YXYOGP/prj2Lc8mT6+Tvg/JY8SIQVw4f5ns7Gyj4y+OGcbWLbtNotXcmufO/ZglS/7m3r0HJtUL\n5mmXCzJ81BB2bjPdqoGCVMTxk0BQVkyWOgawkSQpFN2ktRvQW3/8VWCvLMvfSJKkBGz1xysDwbIs\nfypJ0nbga6Afuqjx1bIs75QkaS76dC9gSB3zkiRJeaOkX2VZXvmEeqdJkjQWqA34ybKct9ZymyzL\n/9Pf72tgkizLv0mStBPYLcvyFv05AAtZljtIkjQY+BIoEmIkSdLbwNsAdpVcsLF6eFREsUEIhd84\nF2Mky5AQn0ibZr1JS0unpVczVq1bSvdOQw1vsAFGjBrMlHc+faiGMlHcH1CKN+YJqkRe6j0eZ5fq\n/LTqW/btOkBqctqjC5qA4qWXX2RcSRQXqVJYZ7E2yCTEJ+LVrCdpqem08mrGmv9+5/mOg43qiDko\nS7U4e+gsjVo9x887fiQjJYOrZ6+hydWYVmBhHuM7fhTVazix8Le5fD51wdOpR49Rf0uq415tmqPV\naOnQrC9VHezZvGcVIQePExNlvsFlaZ63tiO6UrNlPZa+pMs1GXb4ArVa1mPqtvncSckk8uwNtJry\niVYsSXO3F3pQr0UDvnzpM6PjDjWq8cHP01j68S/l1r6U9r6ONRyp27gOpw6aZyXU49SFktq9WXOm\nsnzpSkNkZx4WFkpaeTVj1icLOHP6PN8u+pyPpr/Dwq9/KYPOJ2x75YeX7dFzBGp1As7OTvj7beDa\n9ZuEhJg+nUJF5rF8o4cwov0YkhNScK/lxrLNPxN+9RZxUWZIjVWGdjgPr7YtuH//AWH6JeyZGbeZ\nM+Nrlq74AVmr5czJUGrW8Sx6kSeV/Diai//DADh9+jwd2w/kuUb1+fOvxQQGBJOVlY1Wq6Vr56FU\nrWrHuvV/0KTpc1y9EmYy3TrtZeurR3V42VAvlmz6kVvXIsxSLx5LZ4lt3IcsX1a0jQPQarV07zIM\n+6p2/Lt+OU2aNuTqlRtF7Eqvt+ixx60TSgsl7Tu1YXDvl7h//wGbdqzgYuhlQg6dYPaMBSz/50dk\nrZbTJ0OpZcJ6/Kg/4Nn34Sra+Kn4vq40ONZwpF7jupw0k28BFXNc/eS+hm6c17pZL4PmNeuW0bXT\nEIPmMSPfxNraij/+Xky3Hp04eKBMWX4fW++jnskmTRqy4OtZDPMumpp19Ghv3nzTtHuTmENzy5ZN\nqVe/Np9+usCQz92UmKtdzmP4yEFMfdc8+dmLoyKOn/4vIsvll+O8omOO1DGNgYHAGknXip0CJurz\noreQZTlv7XY2kPfa7CJwUJblHP3vdR5yn4KpY550kh3yU8fUACpLkpS3i0tzSZIOS5J0EXgNaPaQ\na2zT/3umJM2yLP8ly3I7WZbbPWqSHXT599w93Ayf3T1ciY9PNLZRJeCht1EqldjZ25GWlk52dg5p\naekAXAi9TGREDPUb1DWUa9q8ERYWFlwIvfxIHU9KoioRF/f86C0XtxokxZc+qiIpIZnw6xG06WT+\ndBB5xKsScfXIfzPs6l6DxPikh5QoH1SqeNw986OA3N1dDUvYCtp4eObXEXt7O9JS9XUkVVdHzode\nJjIimgYF6oi5SFYnG0WhV3erTkpCykNKGLP+tw28N3AKs1+bgyRBXIT5coeDbsBSlnpcuYoty/79\nkd8W/cWFs+Z73goSr0rArUD9dXN3IaFQ/VWrEnDXR2zo2o4qpKdlMHz0YIKDjpCbm0tKcipnTpyj\npdfDmr6ykx6fioN7fjSDg5sjmYlFB4UNn29O3ykvsOLNH9Bk5xqO71u2gx8Hz+LPcQuRJInkCHWR\nsqYmNT4FJ7f8DVkd3ZxITUgtYtfi+VaMnPIii978htwCmm2q2DB75ResX/wvN86ZdsKpJJLVyUZR\ncc6uzqTEF9X8MHp6dyfE/6jZXnCpVPGGPg2K7/dUcfF46Ns9pVKJfdUqpKWm07ZdK+YtmEnopQO8\n+94Epn38Lm++PRZVXDyquHhDhKePj3+Z63RcrBpPz/yNpTw83FCpE4rY1NTbKJVKqla1JzU1jdi4\nomXV+qhmtf4aSUkp7PDxo337sm/a+v8bieokahToQ2q4OZeqTU7W9zeqaDVnj4byXPOGJtcIZWuH\n8/B+YaAhbUwe+/ceZET/13hh4DjCb0YSGR6NqVDFxePpWfD5c0Nd+PlT5dvk+RSpel8ij7Dr4dy9\ne4+mTRsZHc/IuE3I4RP07Wf6jcx19SK/r67hVp3kUkTyFqwX546F0rB5A5NrhLz2q1AbV9hviyvk\nt+nbuHbtW/HVgpmcvxzM5PcmMH3GZN56x3gyKlP/Hffpa5rvWK0yHovo6nHRsUheRLrBz0zLQK1K\n4PiR06SlpvPg/gOCAg/TvJUu82agfzDe/V5h2IDXCL8ZScQt09XjglREH66ijZ+S1Ek4F1it4+zq\nTHL84/v1AL28e3DY/4hZg2cq4rhaFRePR4HVHu4eLsVojjfSbF+i5mgjzaDb38HfN4hBg02z0W9c\nXDyeHsb+TXHtW55N4T7E3cOV9Rv+5K03pxMRYdwmtGjRBAsLJaHnLplEqzk1d+jYhtatW3Dlagj7\n9m+mQcO6+PlvMJlmc7XLkFeXlVw8f8VkegtSEcdPAkFZMUvqGFmWjwHVAWdZlg8B3dFtYLpWkqS8\nvOo5cv7rKC2QpS+rpfSR9rnk/y2l2u1MP7nvr9cIsArdRq4tgK8ecb28hHIaTLQ64NzZi9SrX5ta\ntT2wtLRkxMjB7PU1XlK31zeIMa/qNjnxHjGAkEPHAXByqmbYQbx2HU/q1a9NVGT+JhMjRw9h+5Y9\nppBZIpdDr1GrnifutdywsLRgwIg+BAeEPFbZGm7OWFfS5Tizq2qHV/sWRN40jxNeHBfPXaFO3Zp4\n1nLH0tKCISP6s9//0KMLPmXOnblIvXp1qFXbE0tLS14YNQR/3/1GNv6+Qbz8im6jnmEjBnL44DGg\ncB2pSb36dYgsUEfMxfXzYXjUccelpgsWlhb0HNaD44HHH6usQqHAzkGXK7hu4zrUbVKXM4fMs2lS\nHv+PvfsMi+L6/z7+nl3AWDEmRgVr7CWW2DuoWMEuJnajJpoYY+89tlhii5qfJfbeFUHBgoIligoW\nBCsWwA4aTVRY5n6wsLKABXZX3Pv/fV0XV8LumdkP45k5Z87OnLkYeIkCX+bDMb4eN27ZAF9vv/da\n1sbWhjnLf2P3Zi98dlvudtikgs5epNCXBciX3xFbWxvcWjXGx8vXqMz+vb60+aY5AE2bu3DMTz/f\nYfidSGrU1s+VlzFTRipUKsu1KzewpNtB18hZMDc58uZEa6ulglsNLvgY/7s6li5Iuym9WNZzBs8e\nvZ7KRtEoZMqeBYA8JfKTp0R+Qv3OWTQvwNWgK+QplIcv8n2Bja0NNd1qE+BjPGdkwdKF+H5qH37r\nMZmnj14PntnY2jBk8QgObz3ECU/TryB6XyFBoTgWciR3vtzY2Nrg3KIux3yOp2odzi2cObTTMtPG\nAJw5fZ4vC78+prVu04y9e4yPaV6eB/imQ2sAWrRsjN9h/fGjWaMOlC/jTPkyzvy5cAWzZ/3J0sVr\nuH//IeHhkRQpqj/BrFu3utGDB9PiVEAgRYoUomDBfNja2tLevQUeHt5GZTw8vOncuR0Abdo045Dv\nUcPr7d1bYGdnR8GC+ShSpBAnT50lU6aMZMmSGYBMmTLi0qAuFy+GmpTz/0eXAkPJVygveeLrsUuL\nevh5v99+lNU+C7Z2tgDY57CnbOUy3Ej0gEFzMuU4DPqr7Jq1aJhsoP2zz/XTamWzz0rn79qzYc02\nzOX06XN8WbggBeL3vzZtXfHcs9+ojOeeA3zbUf/AtJatmnA4vk9RoEBetFotAPnyOVC02JfcvHWH\nzz7Pgb29vt3+5JMMODnXNHogrLmEBIaQt5CjoV7Ub1EPf+/3O74Z1YtPs/FV5TKEXb75jqXS5szp\ncxQuXOD1Ma5tM7yS9dsO8G1Hfb+tRavGHIk/xjVt+C3lSjtRrrQTixau4PeZi1jyv9V89nkOshlt\n4xpcuWyebRx45gKFCuePr8e2tGjdFG8v4zbAe+8h2n3bAoBmLRoaHs57+MBRSpYuxicZP0Gr1VKt\nZiXDw/oS6rG9fTa69viG9au2mCVvUtbYh7O286fQoFDyJupb1GvhxDGf1PVt6reox4Gdlt3G1nhe\nffbMeQol6hO1bN2MvUky7/U8SPsOrd4jc0Fuht0mc+ZM5Mql/7Jaq9XSoGFdsx0vTp8OonCR121I\n27Zu7Nlj/EDNPZ4+dOykb0NatWrK4cP6umJvn41tW5czbux0TpxIfm7Xrl1zNm/ebZacls68dMka\nihSuSqmStWhQvx1Xr9ygSeNvMBdLHZcBWrRpapYH476JNZ4/CWEqc04dY6AoSglACzxSFKUAEK6q\n6hJFUTIDXwOr3nNV/wDv8zSuMKAicBJom8qsClADSJg6JisQqSiKLfor2hPmTnjfLCbR6XSMGPwr\nG7YtQ6vVsH7NVkJDrjJ05M8Enb3APq9DrFu9hT8WT+fE2X1ERz3hh+/0T0avVrMyQ0f+jC5Why5O\nx9AB442ukmreqgkd2n5v8fy/jZzNwvW/o9Fq2bneg+uhN+gztCfBgSEc9vanVPkS/P7XVLJlz0od\nl5r0HtKTtnU7UahoQQaO75twnz2rFq3naoj5T8zeln3iiBks2zQfrUbLlvW7uBp6nX7DfuBC4CUO\n7jvCV+VLsWDlDLLZZ8O5YW36Df2eZrXf+PgAi+UcPmQim7cvQ6PVsm71FkJDrjJ8VD8Cz1xgr9dB\n1q7azMLFMzgZ6EN01BPDk+ir16zM8FG/EBurI06nY3D/sUZ1xFLidHEsGLOIKWsmodFq8d7ozc3L\nt+gyqDOXz13mhM/fFCtXjLFLxpDVPgvVGlSly8BOfN+gN1pbLbO2zgTg32f/8lu/GRafJkSn0zFl\n5CwWrZ+DVqthx3oProXe4MehvQgOvISvtz+ly5dkzl/TyJY9K3VdatFnSE9a1+1Io+b1+bpaeew/\nzUbz9vpnOY/5ZRKhF02/nftdmccOm8KqzYvQarVsWreDK6HXGDj8R84FBrN/ry8b12xn9qIpHD7l\nQXT0E/r2HArAqmUbmDn/V3yObkNRFDav20mIGW4/f5s4XRzbxi7n+1Uj0Wg1nNx0iHtX7tB4QDtu\nn7/Oxf2ncRvRkQyZMtB1YX8AosIf8levmWhtbei7eTwAL5/9x9oBf3yQqWPidHEsG7uYUavGo9Fq\nOLTpAHeu3Kb9wA5cO3eVgP0n6TyyO59kysighfpt+zDiIb/1nEx115qUrFKarNmz4txWP7PagsHz\nCAu27Bcacbo45o/5g9/WTkGj0eC1cR83L9+k2+AuhAZd5rjPCYqXK8aEpePIYp+V6i7V6DqwMz3q\n69uKXHlz8YVDToKOW+6LDJ1Ox9DBE9iy4y+0Gi1rV28hJOQqI0b9wtmz59nreZA1qzbz55KZBATu\nJyoqmp7d330b8bDBv/K/pbOws7MlLOw2ffuYdkusTqfjl/6j2bNnHVqNhhUrNxIcfJlx4wZz+nQQ\nHh4+/LV8AytWzONSsD9RUdF07PQjoH9g1uYtuzkXdIhYnY5+v4wiLi6OXLlysmXzMgC0Nlo2bNiB\nt7cvAC1aNGbO7EnkzJmDnTtXERR0kWauH2YO9yHjpnHq7Dmio59Sv2UnfuzRmTZJHvz6Iel0OmaO\nmsvcdTPQaDV4bPDixuUweg3pTkhQKH7exyhZrji/LZtE1uxZqOVSnV6Du9HBuTsFixZg2G+DUOPi\nUDQaVi1YR9gVywyomnIcBqhaoyKREfeSTds1bsowSpUpBsDcGf/jxjXz5dfpdAwZNJ7tO1ei1WpY\nvWozIZeuMGp0f86cOY+X5wFWrdzI4qW/E3juIFFRT+jetR8A1WtUYsDA3sTExhIXF8fA/mN5/CiK\n0mVK8OfiGWi1WjQahe1bPdm71/wDajpdHLNHz+f3db+h1Wjx2KivFz0HdyMk6DL+PscoUa44U5dN\nJKt9Fmq6VKfnoG50qvcdBYoWYOi0AcSpKhpFYc0f6y1aL4YOmsDWHcvRarWsXa3fxiNG/0LgmQt4\neR5g9cpN/Ll0FqeDDhAVFU2Pbv3fus7cuXKycPEMtFoNGo2G7ds82bfXPF+I6nQ6Rg+dzLqti9Fo\nNWxcu53LIdcYPKIvQYEX8fE6xIbVW5n35zT8T3sRHfWEH3sMBuDJk6csXrgSzwMbUVE56OPHAW/9\nRSsTp42gVGn9HQ+zZyziuhnrcdL81tiHs6bzJ50ujrlj5jNj7bT4vsVewi7fpPvgroQGXeaYz3GK\nlyvOpKXjyWKfheou1ek2sCvd6/cEIHfeXOS0cN9Cn9P6zqv1mSeyadtSNFqtIfOwkf0IPHuBfV4H\nWbt6i/4876w3UVFP+P671+d5w0b205/nxekYPGAc0VFPyJnzM1ZvWISdnR1arQb/IydY8Zd5rrbW\n6XQMGjiWnbtWodVqWbVqE5cuXWH0mAGcOXMezz37WbliE0uX/c65875ERUXTtcvPAPzQuwtfFi7A\n8BH9GD5C3640d+vMgwf6uyNat2lG61bdzZLzQ2W2FEsdl0H/ZU1n9z4Wy26N509CT02/58hbPcVc\ncxwpiqJDP+0L6CduG6mq6h5FUboCQ4AY4BnQRVXVG4qiPFNVNUv8suOBZ6qqzoz//ZmqqlkURckB\n7ANsgalARhLN2Z7os0sAm+LXfxDopKpqQUVRnIDBqqq6xs/vXklV1b7xn9cLeBC/7nPAd6qq/qco\nSh9gKHAz/u/JqqpqN0VRagJL0F/F3hZYFr/uAEVRPgcCVFUt+LZtlMu+hFVNKOWQ0fIPFzS35zrL\nPLXekh6/tOxDJ82ton3hdxf6yETGRL+70EfmSYxl5803tzZZS7270EfmtvpfekdIlcc668oLcPaJ\n9XWE/3mZfC7kj9m/Ee93pebHpnbZ5A/j+phFvkzdlEvpLerFs/SOkGpfZS+Y3hFSLfjJh7vz0hwy\n2WZI7wip9lmGbOkdIVW0lrlh3KLsbUx7WHh6uPTPnfSOkCpxWNUwAAD/xljfebW1yZ4hc3pHSLUa\n2SwzzZolbb65UyYbT4OrpRpZ34EriSLB+9Ll395sV7Srqqp9w+srgZUpvJ4l0f+PT+k9VVUfA5WT\nLLqOUwB1AAAgAElEQVQihXWFAGUTvTQ6/nVfwDf+/1ckLBv/eUafmWhdi4BFKbx+FP2DWhM4JXrv\nIW+fV14IIYQQQgghhBBCCCHE/6es7yt3IYQQQgghhBBCCCGEEOIjYpE52oUQQgghhBBCCCGEEEJY\nlzhVZtxJK7miXQghhBBCCCGEEEIIIYQwgQy0CyGEEEIIIYQQQgghhBAmkIF2IYQQQgghhBBCCCGE\nEMIEMke7EEIIIYQQQgghhBBCCFSZoz3N5Ip2IYQQQgghhBBCCCGEEMIEMtAuhBBCCCGEEEIIIYQQ\nQphABtqFEEIIIYQQQgghhBBCCBPIHO1CCCGEEEIIIYQQQgghUONkjva0kivahRBCCCGEEEIIIYQQ\nQggTKKqqpneG/zNs7BytamNnss2Q3hFSTVGs71u3fJlzpneEVLn9/EF6R0i1F7Gv0jtCqmkU6/oe\nNEYXm94RUs3ajhZ2NrbpHSHVXsXGpHeEVNNqtOkdIVX+ueOb3hH+z3Ao3CS9I7y3Jy+ep3eEVMti\nlzG9I6SaTo1L7wip8soK22qNlfXt46zw3DqjjV16R0i1lzrr6l9YY38ooxWOBVgbGyvrcwL8G/My\nvSOk2osXt6yrIflIhBRran0NWhIlLnumy7+9TB0jhBBCCGGlapf9Lr0jpJrfub/SO4IQQgghhBBC\nmJ0MtAshhBBCCCGEEEIIIYTACm/Q+mhY19wEQgghhBBCCCGEEEIIIcRHRgbahRBCCCGEEEIIIYQQ\nQggTyNQxQgghhBBCCCGEEEIIIVDj5BmyaSVXtAshhBBCCCGEEEIIIYQQJpCBdiGEEEIIIYQQQggh\nhBDCBDLQLoQQQgghhBBCCCGEEEKYQOZoF0IIIYQQQgghhBBCCEGcKnO0p5Vc0S6EEEIIIYQQQggh\nhBBCmEAG2oUQQgghhBBCCCGEEEIIE8hAuxBCCCGEEEIIIYQQQghhApmjXQghhBBCCCGEEEIIIQSq\nzNGeZnJF+0eiUUMnLl44QkiwP0OH/JTsfTs7O9atXURIsD/H/HdToEBew3vDhvYlJNifixeO0NCl\nruH1JYtnEXEniMCzB4zWVa5caY767SbglDcnjntSuVJ5k/M3cKnD6bP7CTx3kAGDeqeYf/nKeQSe\nO8hB323kz+8IQMWKZfE/7oH/cQ+OntiDq1tDADJksOPQ4e0cPbGHv0/tZeSo/iZnTKx+gzoEnPHh\nbNBBBgz84Y15zwYd5MChrYa8X1csi9+x3fgd243/cQ9DXoAff+rOiVNeHD/pxbLlc8iQwc6smROr\n6VyN3Uc34nliMz1+7pzs/YrVyrPJZyWB4f64uDobvffn+tkcu+zDgjUzLZYvgSW2s719Vlat+YNT\nZ7w5eXoflatUMFvehi5OnD/nS/BFPwYP/jHFvGtWLyT4oh9+R3YZ9sMcObKzb99GHj0MYc7sX1Nc\n99Ytf3Hm9H6zZU3g4lKXoKCDXLhwmMGD+6SYefXqP7hw4TBHjuwgf3595nr1anH0qAenTu3j6FEP\n6tatYVhm586V/P23F6dP+zBv3mQ0GtOaCnMf3/LmdWC/92bOn/MlKPAgP/ftYSg/dsxAbt4IIOCU\nNwGnvGnSuF6aMjds6MSFC0e4FOzPkDdkXrt2EZeC/TmaJPPQoX25FOzPhQtHcEl0TL5y+QRnz+w3\nHHsTrF27yJD3yuUTBJzyTlPmxFxc6nI28ADnzvsyaFDK9WLlqj84d94X38PG9cL/6G5OntyL/9Hd\n1K1b3bCMra0t8/+YQmDQQc6cPUCLFo1Nyvght3G5cqXxN3O75+JSl3PnDnHx4pE3Hi9Wr17AxYtH\nOHJkZ5LjxQYePrzE7NkTjZZp29aNU6f2cebMfiZPHmlyxrep5lSFjX6r2Hx0LZ37dkj2fvmqZVm5\nbzH+tw7g3Kyu0XtHbx9glc9SVvksZcaKyRbN+b5GT/mdOs2+oWWn5H2QD6le/docD9jLybPe9BvQ\nK9n7dna2LFk+m5Nnvdl7YBP54tu9fPkduXU3iEN+Ozjkt4MZsycYltnhsYrjAXsN733+eQ6L5W/Y\n0IkL5w8THOzPkMFv2C/XLCQ42B9/v91G9dp73yYePwplzpxJFssHUL9Bbf4+s4+AwP38MvD7FDMu\nWzGHgMD9+BzcYtjGCRzz5uFWZCB9++nbjgwZ7PA5tIUjx3Zx7KQnw0f2s0Bm6+p3WkPfIqXMH3u7\nl1Jmc2/n8eOHcOXKcR48CDZrVjD/vpdAo9Hg67+T9ZsXmz2ztdQLc/eJihUrbOhbBpzy5tHDEPr9\n3BPQ15Ezp30IOOWN55515MmTy6Ts5h4HcHTMg4fnWk6d9ubvU3vp82M3k/J9iMwJNBoNfsd2s2nL\nUrNntsT+F3jhEP4nPDh8dBcHDm8za15L9JPd3ZsTEODNqVP72LVrFZ999qlZMwthLib3cBRF0SmK\nEqgoSpCiKGcURanx7qXeuc7yiqI0TfR7N0VR/khSxldRlErvWI+hjKIo7RRFuaQoyiFFUZwURXkS\nn/ucoij7FUX5IpWZxiuKMjhtf6ExjUbDvLmTcXXrxFflnGnfviUlSxY1KvNd92+JinpCiVK1mDNv\nCVOnjAKgZMmiuLu3oGz5ejRz7cj8eVMMHddVqzbRzLVjss+bNmUUv076nUqVGzJhwkymTR1lcv5Z\nv0+gTavuVK7YiLbt3CheoohRmS5d3YmOfkr5svVY8MdfTPh1GADBwZepW6sFtaq70rplN+bOn4RW\nq+Xly1e4Nu1IzWrNqFndlQYudahc2fSBkdd5x9O29XdUqdSINinmbUd09BMqlKvHwgXLDXkvBV/G\nqXZLatdwo03L7syZp8+bJ08uevfpilPtllSv0gStVkObtm5myZtS/tHTBtOnwwCa1/6Wpq0a8mWx\ngkZlIsPvMfqXX/HclnzAbvnCtYzoOyHZ65bIae7tDDBt+lj2+xyh8tcNqVnNlcuhV82Wd+7cSTRv\n0YVy5evR3r0FJUoY74fdu31DdHQ0pUrXZt78pUyepB8Ie/HiJRMmzGT48JQHF1q0aMyz58/NkjNp\n5jlzfqVFi65UqNCAdu2aJ8vcrVt7oqKeUKZMXebPX8bkycMBePQoirZtv6Ny5Ub06jWQv/6abVim\nU6efqFq1CRUrupAz52e0adPMpIzmPr7FxsYyZOgEvirrRM1abvTp081onXPnLaFS5YZUqtwQr70H\n05zZza0TZcs5880bMkdHPaFkqVrMnbeEKYkyt3dvQbny9XBNckwGaODSjkqVG1KtuqE5oWPHPoa8\n27d7sn2HJ6bQaDT8PnsirVp2o+LXLvH1wnjf69rNnejoJ5T9yok/5i/j10mJ60UPqlRpzPe9BrF0\n2et6MXRYXx48eET5cvWo+HUD/P3/Ninjh9zGUxO1e+MnzGSqGdq9uXMn0aJFV8qXr4+7e8r7XnT0\nE0qXrsP8+UuZNGkEkHC8mMXw4cYD1DlyZGfq1JE0afItX3/dgFy5PsfZuaZJOd+Wf/CUXxjQcRjf\nOnWlYYt6FCxawKjMvfD7/Np/Gt7bk39B+PLFK7q49KSLS0+GdDNtW5pLy6Yu/Pm7ZQd430Wj0TBt\n1li+aduTmlWa0aqNK8WKFzYq07FLO6Kjn1KlQkP+XLiCsRNedyXDbtzCuXZLnGu3ZMiAcUbL9e41\n2PDew4ePLZZ/7txJuDXvTLlyzrRv34KSSdvB7t8QFf2EUqVqMW/eEqZMft0Ojp8wg2HDU/6y2ZwZ\np88aj3vrnlSv3IQ2bV0pXtz4+NapS1uio59SqXwDFi1YzviJQ4zenzJtFAd8jhh+f/nyFS1du1Cn\nRnPq1GhO/QZ1qGSmPmdCZmvqd1pD3yKlzB97u5dSZktsZ0/P/dSu3cJsORPnNfe+l6D3j125HHrN\nIpmtoV5Yok90+fI1Q9+yStXG/Pvvf+zY6QXArFmL+LqiC5UqN8TTcz+jRw0wKbu5xwFidbGMGjmF\nyhUbUt+5Db2+75xsnaawROYEfX7qbrG6bKn9r3mzztSt2Zz6dVubNa+5+8larZaZM8fTqFF7Kldu\nxPnzIfTp081smYUwJ3NcSvCfqqrlVVUtB4wAppphneWBpu8slTo9gB9VVU24vNcvPndZ4BSQ/Ktj\ny2cCoErlCly7FsaNG7eIiYlh06adNHdrZFSmuVtDVq/eDMDWrXuo51wr/vVGbNq0k1evXhEWdptr\n18KoUll/ha+f/988jopO9nmqqpI1W1YAstlnJSLynkn5K1Uqx/XrNwkLu01MTAxbt3jQzNXFqEwz\n1wasX7sVgB3bvXBy0n8f899/L9DpdAB8kiEDqvp6mefP/wXA1tYGG1sb1MRvmqBikrzbtnjQrFkD\nozJNmzVg3dpthrx1naonz/tJBqNMWhsbMmb8BK1WS8aMGblr4nZ9k6++LsWtG3e4czOC2JhYvHb4\nUK9xHaMyEbcjuRx8lbi45Nvsb78A/n32r0WyJWaJ7Zw1axZq1qzMqpWbAIiJieHJk3/Mkrdy5fLG\n++HmXbgluUrBza0hq9dsAWDbtj2GQbB///2PY8dO8eLly2TrzZw5E7/80oupU+eZJWdKmRO28ebN\nu3FNsu+5urqwNn7f27bNEycnfeagoItERt4H9J3GDBkyYGenvxrun3+eAWBjY4Otra1J+54ljm93\n797nbOAFAJ49e05IyBUcHXKnOeO7Mm/ctBO3JJnd3pDZza0RG99wTH4fbdu6sXHjTpPyV6pUnuvX\nXu97W7bsxtXVuC67NmvI2jX6erF9u6fhmBwUdJG7b6gXXbq0Y+aMhYC+HXn0KCrNGT/0NlZVlWzx\n7Z69Gdq9pMeLzZt3p3i8WGM4XngmO168fPnCqHyhQvm5cuWGYRD14EF/WrZsYlLONylVoQR3wsKJ\nuBVJbEwsPjsPUqeR8aB+5J27XL10HTWFduRjVKn8V9jH/xunl68rliXs+k1uht0hJiaGHdv20KRZ\nfaMyTZrWY+O67QDs3rGP2omukkxvydrBTTtTbgcT9stte3CO3y8N7eCL5O2gOVWsVJYb129yM6Fv\nsXUPTVyNt3HTZg3YsE7ft9i5Yy91nF5v46auDQgLu03IpStGy1iqz6nPbF39TmvoWyRlDe1eUpba\nzidPnuXu3ftmy5nAUvueg0NuXBo5sTq+b29O1lIvLN0nqlevFtev3+TWrXDg9b4IkClzJpP2RUuM\nA9y7+4CgwIuAvp8fGnoVBzP28y01duHgkJtGjZ1ZuWKj2bImsNT+ZymW6CcrioKiKGTOnAmAbNmy\nEGmh8RYhTGXuqWOyAVEAiqLkURTlSPxV4xcURakd//ozRVF+UxTldPyV5FXirzy/rihKc0VR7ICJ\nQPv4Zdu/60MVRVmkKEqAoigXFUVJdqmuoihjgVrAn4qizEjyngJkTZS7iqIoxxRFORv/3+JvyVQq\nUfY032fq4Jib23ciDL/fCY9M1pgkLqPT6Xjy5CmfffYpDg4pLOv49oZo4OBx/DZ1NDeunWL6tDGM\nGm3adyN5HHJz506k4feI8EgcktyClschl6GMTqfj6dN/yBF/q0+lSuX4+9Rejp/0on+/0YbGS6PR\n4H/cg2thpzh08CgBAUEm5Uzg4JCL8ER5w8Pvkschad7chjI6nY6nT17nrVipHCdOeXHsb08G/DIG\nnU5HZOQ95s9byoVLfly+dpynT//h4EF/s+RN6ovcObkb8boDfS/iPl/kzmmRzzKFJbZzwYL5ePjw\nMQv/nI7f0V3M/2MKmTJlNFNe430pPDwy2eCtg0Nu7iTaD58+/eedt6yNHzeEOXOW8N9//5klZ/I8\nibdxJI6Oqc/cqlVTgoIu8urVK8Nru3at4tatMzx79pxt29J+hbWlj28FCuSlfLky/H3yrOG1H/t0\n58xpH5YsnkX27PZpynznXXXhDZkdHZIvm5BZVVW8PNfz9wkvevZIfrdRrVpVuX//AVev3kh1ZqNs\nDrm4E26cIem+l7jMm+pFy5ZNOBdfL+ztswEwduwgjh7zYPWaBXzxxedpz/iBt/GgweOYNnU016+d\n4rdpYxhtYrvnkFKGZNs4dceLa9duUqxYYQoUyItWq8XNrSF58zqYlPNNcubOyf2IB4bf70c+IGee\n929H7DLYsdzrfyzdvZA6jWtZIqJVyuOQi/Dwu4bfI8LvJbslP3eeXISHJ+kP5dDXi/wF8nLQbzs7\n96ymWvWKRsvNWzCFQ347GDgk+e3X5uLokIc7t43bbQfHPEnK5Dbqzz15+vSD3rqdJ09uw/YDiAi/\nm2wb53HIRfidu4aMT588I8dnn5IpU0Z+GfA906fOT7ZejUbD4aO7CL1+At9DRzltpj4nWF+/0xr6\nFskzf/ztXvLMltvOlmCpfW/Kb6MYP2Y6cXFxZs9sLfXCUn2iBO3dW7Bx4w6j1yZOHMb1a6f49ttW\njJ9gNDySKpYaB0iQP78jZcuVJuBUYJozfqjM06aPYeyoaRapy5ba/1RVZeuO5Rw8sp2u3d857Pbe\nLNFPjo2NpV+/UQQEeHPjRgAlSxZl+fINZsssklNV6/9JL+YYaM8YP/gcAiwFEu4Z7QDsU1W1PFAO\nSDg6ZgZ8VVWtCPwDTAJcgFbARFVVXwFjgY3xV5wnfCWYMMgdqChKIJB42phRqqpWAsoCdRVFKZs4\noKqqE4EAoKOqqgn30NSOX88toAHwV/zrIUAdVVUrxOeY8pZMJYBGQBVgnKIotkk3jqIo38d/CRAQ\nF5fy1BH6sX5jSb9ZTrnM+y2b1A/fd2HQkPEUKlyZQUMmsOR/s95a/l1SiJA8PykWAiAgIIiqlRvj\nVKclgwb3McwxGRcXR63qrpQsVoOKFctSslQxk3K+zpvytjQuk1JcfaHTAUFUq9wE57qtGDioNxky\n2JE9ezaaNWtA2TJOFC9Sg0yZMuHe3vy3beqzpZDfIp9kGktsZxsbG8qVL82ypWupXbM5z//9L8V5\n9cyXN+l++Oa8KSlbthSFCxdg1669JudLyXvte+/4u0qWLMqkScPp23eEUZnmzbtQqFBlMmSwM1zF\nkbaMlju+Zc6ciU0blzBw8DjD1Tl//m8VxUrUoGKlhty9e58Z08d+NJnrOrWkStXGuLp1ok+fbtSq\nVdWo3DftW7LBxKvZ35zt3Ttf0nrx66Th/PyzfloIGxstefM6cPx4ADVruHLy7zNMmZL2OcQ/9Db+\n4fsuDB4yni8LV2bwkAksNrndS2v+Nx8voqOf0K/fKFavXsCBA1u4efMOsbGxJuV8k5SOHanpjbas\n7E73Jj8w9qdfGTChL44FLPOFgLUxpV7cu3ufCqWdqVe7FWNGTePPpbPIkjUzoJ82pm6N5rg26Ui1\nGhVx/8ZS/Yvkr5lar83NlIzDR/Vj0R/LDVevJxYXF0fdms0pU6I2X1csm2zaBtMyW1e/0xr6FklZ\nQ7uXPHPy18y1nS3BEvtew8bOPHjwyHD1srlZS72wZF/Z1tYWV9eGbNnqYVRm7Njf+LJwZdav386P\nP3ZPa3SLjQOAvp+/et1Chg/91egqfFNZInPjxvV4+OARgfF33Jqbpdq+Ji7f4Fy7Je6te9CjV0eq\n16xsprzm7yfb2Njw/fedqVatKYUKVeL8+UsMHfquSSmESB/mnDqmBNAYWBV/lfgpoLuiKOOBr1RV\nTZjf4RWQMOp0HjisqmpM/P8XfMvnJAxyl48fvA9I9J67oihngLNAaaDUe+ROmDomH7AcmB7/uj2w\nWVGUC8Ds+PW9yR5VVV+qqvoQuA8ke5KIqqqLVVWtpKpqJY0mc4orCb8TSb5EV63ldcyT7DaYxGW0\nWi329tl4/DiK8PAUlo14+y00XTq3Y/t2/VUkW7bsNnnu84jwu+TN+/qKJwfHPEQmuWUxIuJ1Ga1W\nS7ZsWXn82Hham8uh13j+/F9KlSpu9PqTJ//g7/c3DVyMp0dJq/DwuzgmyuvomDvZ7bYRicpotVqy\n2WclKqW8//5HqVLFcXKuyc2w2zx6+JjY2Fh279pH1WpfmyVvUvci75Pb4fUjBXI5fMGDuw/eskT6\nsMR2Dg+PJDz8ruFKs507vChX7m27aGryGu9Ljo55kk0vER5+13CF6ZvqcWLVqlakQoWyhIYe4+CB\nbRQtWghvb/PdGhueZN9zdMxDRETSzJFvzOzomJuNGxfTs+dAbty4lWz9L1++xMPDJ9mtfqnKaKHj\nm42NDZs3LmH9+u3s2OFlKHP//kPi4uJQVZWly9am6fgWfifS6EriFOvCGzLfCU++bELmhL/7wYNH\n7NjpZZRNq9XSsmUTNm/eleq8yfKH3yWvo3GGhNufE0QkKpO0Xjg45mb9hv/RK1G9ePQoiufP/2XX\nrn2A/hbPcuXLpD3jB97Gnc3c7oWnlCHJNn7bvvcmnp77qVOnBU5Orbhy5TpXr4aZlPNN7kc+4AuH\n11ewf5EnJw/uPnzv5R/eewRAxK1IzhwLpFgZ8w1KWrOI8LtGV6Q6OOZKNoVDZMRdHB2N+0NRUdG8\nehVDVPx0f+cCLxJ24xaFixQCMOy/z589Z9tmD76uaHQ9idncCY8kbz7jdjsy4m7yMonabfts2d5Z\nr80pItH2A/3xKuk21vctchsyZrPPQtTjaCpWKsf4X4cSeOEQvX/sxoBBven5fSejZZ8++Yejfn9T\n30x9TrC+fqc19C1SzPyRt3spZrbgdjY3S+x7Vat9TZOm9Qm8cIilK+ZQu041/lwy02yZraVeWKpP\nBNC4sTNnz57n/v2U2/gNG7bTqlXaZ8e11DiAjY0Na9YtZNPGXeyO39bmYonMVatXpEmz+pwPPsLy\nlfOoU7c6S5b9br7MFmr7Etbx8OFj9uz2oaKZ+heW6CeXK6cf4rt+/SYAW7d6UK1axTeWFyI9mXXq\nGFVVjwOfAzlVVT0C1AHCgdWKonSJLxajvv6qKg54Gb9sHGCT2s9UFKUQMBioHz/f+h7gk1SuZld8\nVtBfkX9IVdUygNs71pV4IkodacgPcCogkCJFClGwYD5sbW1xd2/Bbg/jh1ju9vCmc+d2ALRp04xD\nvkcNr7u7t8DOzo6CBfNRpEghTp46m+wzEouIvEfdOvo5u+o51+KKidMUnD59ji8LF6RAgbzY2trS\npq0rnnuMH57muecA33ZsA0DLVk04fPg4gOEWeYB8+RwoWuxLbt66w2ef58DeXj/X6iefZMDJuSZX\nQq+blDPBmdPnKJwob+u2rnh6HjDO63mADh1bG/IeeVPeooW4eesOt29HUKlKeTJm1FeXuk41CLXA\ng1AALpy9RP4v8+GYPw82tjY0aenCoX1+FvksU1hiO9+//5Dw8EiKFNUPPtR1qkFoiHkehhoQEESR\nIgVf74ftmuPh4WNUxsPDh86d2gLQunUzfOP3wzdZvGQ1hb6sRPHiNahXvzVXrtygYUN3s+R9nbkQ\nBQroM7dr58aePcaZ9+zZT8f4fa9166YcPnwMAHv7bGzbtpyxY6dz/Pjr7y0zZ85E7tz6L3K0Wi2N\nGzubVJctdXxbsngWl0KuMmfuYqN1JWQHaNmiCRcvhpqcub17CzySZPZ4Q2YPD2/ap5A5U6aMZMmi\n/7I1U6aMuDSoa5Stfv3ahIZeNbotNK1Onw6icJHX+17btinUC08fOnbS14tWrZLUi63LGTd2OidO\nnDZaxtPzAHXqVAPA2bkmISFpn+fxQ2/jiMh71Ilv95yda5k8PU/CvpeQv107txSPF50Mx4um+Poe\ne+d6c+b8DIDs2e35/vvOLF++3qScb3IpMJR8hfKSJ19ubGxtcGlRDz/vd+cDyGqfBVs7/Q189jns\nKVu5DDcuh1kkp7U5e+Y8hQoXJH/8vteydTP2eho/kHmv50Had2gFgFvLRvgfOQHAZ599aniob4GC\nefmycEFuht1Gq9UappaxsbGhYWMnLllojtWk9drdvUXK7WDCfvke7aC5nTl9ni8TbePWbZqxd49x\n38LL8wDfdND3LVq0bIzfYf02btaoA+XLOFO+jDN/LlzB7Fl/snTxGj77PAfZEvU56zrX4PJl8/Q5\n9Zmtq99pDX2LpKyh3UvKEtvZkiyx7/06fhZlStSmfBlnenbrj9+RE/TuNTjZZ6eVtdQLS/SJErRv\n3zLZtDFF4r/EBXBzbWjSvmiJcQCABYumERp6jQXzl6U524fMPGHcDEoWq8lXperQvWs/jhw+Tq8e\nA82W2RL7X9J+s3P9WlwKvmyWvJboJ0dE3KNEiaJ8/nkOQH/uFGKmcQAhzC1NA8NvoihKCUALPFIU\npQAQrqrqEkVRMgNfA6vec1X/oJ83/X1kA54DTxRFyQU0AXxTFVw/f3tCC2OP/ssBgG5pzJQqOp2O\nX/qPxnPPOrQaDStWbiQ4+DLjxw0m4HQQHh4+/LV8AytXzCMk2J+oqGg6dNLP0RkcfJktW3ZzPugQ\nsTod/X4ZZZgXbM3qBdStU53PP89B2PUAJkycyfIVG+jdewi//z4RGxsbXr54QZ8+Q03OP2TQeLbv\nXIlWq2H1qs2EXLrCqNH9OXPmPF6eB1i1ciOLl/5O4LmDREU9oXtX/ZT21WtUYsDA3sTExhIXF8fA\n/mN5/CiK0mVK8OfiGWi1WjQahe1bPdm79+A7krx/3sGDJrBtxwq0Wg1rVm8h5NIVRo7uz9n4vKtX\nbmLx0lmcDTpIVFQ033X7BYBq1SsxYNAPxMTEosbFMWjAOB4/iuLxoyh27tjLkaO7iI3VcS7oIiv+\nssycYTqdjikjZvK/DXPRajVsX+/BtdAb/DS0FxeDQvDd50eZ8iWZs/w3smXPilPDWvw0pBct63YA\nYOXOPylUpACZMmdk/9ldjB0wmWO+f1skp7m3M8DQQRNYumw2tna2hN24zU8m1t/Eefv3H4PH7jVo\ntVpWrNzIpUuXGTt2EGdOn8Njjw/LV2xg+V9zCL7ox+PH0XTu8vp2tdDQY2TLmhU7O1vc3BrRzLWj\nWU/I3pR5wICx7N69Cq1Wy8qVm7h06QpjxgzkzJlz7NmznxUrNvLXX7O5cOEwUVHRdO7cF4DevZ27\n6xQAACAASURBVLtSuHBBhg//meHDfwbAza0ziqKwZctS7Ozs0Gq1HD58jCVL1piU0dzHt5o1KtO5\nU1vOnQ8m4JT+RGTMmGl47T3ItKmjKVeuFKqqcvPmHfr8OCzNmfckyTxu3GBOJ8q8YsU8LsVn7pgo\n8+YtuzmXJHOuXDnZsll/sqC10bJhww68vX0Nn6mfP9P0aWMS8g8aOJadu/T1YtUqfb0YPWYAZ86c\nx3PPflau2MTSZb9z7rwvUVHRdO2irwM/9O7Cl4ULMHxEP4aP0B+nm7t15sGDR4wZPY2ly35n+vSx\nPHz4mB9+GPK2GO/M+CG3cZ9E7d4LM7V7/fuPYffu1fH7XsLxYiCnT59nzx6f+H1vDhcvHuHx42i6\ndOlrWD409ChZEx0vXF07ERJyhVmzxvPVV/ordqZMmWPyFwJvyz9z1FzmrpuBRqvBY4MXNy6H0WtI\nd0KCQvHzPkbJcsX5bdkksmbPQi2X6vQa3I0Ozt0pWLQAw34bhBoXh6LRsGrBOsKu3LRIztQYMm4a\np86eIzr6KfVbduLHHp1pk+Rhcpam0+kYMXgim7YtRaPVsn7NVkJDrjJsZD8Cz15gn9dB1q7ewsLF\nMzh51puoqCd8/90AAKrXrMywkf2IjdURF6dj8IBxREc9IVOmjGzavhQbG1u0Wg1HfI+zeoX5HxqY\nkL9//zHs8ViLRqth5YqNBF+6zLixgzl9Rr9fLl++gRXL5xIc7E/U42g6dX49Z/zl0ONky6av183d\nGtGsWQcumbkd1Ol0DB08gS07/kKr0bJ29RZCQq4yYtQvnD17nr2eB1mzajN/LplJQOB+oqKi6dl9\nwFvXmStXThb+bzparQaNRsOObV547z1k1szW1O+0hr5FSpk/9nYvpczm3s4PHjxi8uQRtG/fgkyZ\nMnL16gmWL9/A5MlzzJLX3PuepVlLvbBEnwggY8ZPaFC/Dj8m6QtPnjyCYsUKo8bFcfNWOD/9NNyk\n7OYeB6hWvRLfdmjNhQsh+B/XT3kzcfxMvPf5pjmnpTNbmiX2v5xffM7qdQsA/Rf5Wzbt5sB+81y8\nZ6l+8uTJc9i/fzMxMbHcuhVOr17m+zJDJBenpjTXpHgfiqnzKiqKokM/7QuAAoxUVXWPoihdgSFA\nDPAM6KKq6g1FUZ6pqpolftnxwDNVVWfG//5MVdUsiqLkAPYBtsBUICNQSVXVvok+1xcYrKpqgKIo\nK4CqwHX0V5nvUlV1RZIyif/fCdgJ3IjP/AToqarqZUVRqgMrgQfAQaCzqqoFU8hUMkn2C4Crqqph\nb9pWNnaOH+NU2m+UyTZDekdItZTm+vrY5cv88T3I9G1uP//4pql5lxexln1AlCVoFHM/q9qyYnSW\nmUvakqztaGFnk+wxIB+9V7Ex6R0h1bQabXpHSJXyOb5M7wip5nfur3cX+gg5FG6S3hHe25MXKT8X\n6GOWxc48Dzf/kHSq+R96Z0mvrLCt1lhZ3z4uPZ++lkYZbezeXegj81JnXf0La+wPZbTCsQBrY2Nl\nfU6Af2NevrvQR+bFi1vW1ZB8JAILNLe+Bi2J8jd3pcu/vckD7eL9yUC75clAu+XJQPuHIQPtlmdt\nRwsZaP8wZKDd8mSg3fJkoP3DkIF2y5OBdsuTgXbLs8b+kAy0W54MtH8YMtCeNjLQnnbWNZIjhBBC\nCCGEEEIIIYQQQnxkzDpHuxBCCCGEEEIIIYQQQgjrpMoc7WkmV7QLIYQQQgghhBBCCCGEECaQgXYh\nhBBCCCGEEEIIIYQQwgQydYwQQgghhBBCCCGEEEIIrPDZ3h8NuaJdCCGEEEIIIYQQQgghhDCBDLQL\nIYQQQgghhBBCCCGEECaQgXYhhBBCCCGEEEIIIYQQwgQyR7sQQgghhBBCCCGEEEII4lQlvSNYLbmi\nXQghhBBCCCGEEEIIIYQwgQy0CyGEEEIIIYQQQgghhBAmkKljPiCnXGXSO0KqPNO9TO8IqbYme4b0\njpBqP/9jXd93/UG+9I6Qav8o1neo8/pEl94RUuVzbNM7Qqrl1lnX7XBfxKrpHSHVvnnkm94RUi2i\nSaH0jpAqFY48Tu8I/2dEXPNK7wipUqNst/SOkCqdbPKnd4RUWxkTlt4RUmXTp5nTO0KqjX9uXZm/\ne2F9fc7f7P5J7wiplk9rXfXi9IvI9I6Qahceh6V3hFTLkTFrekdIlScv/03vCKl2qXjx9I4gxEfP\n+noCQgghhBDCajkUbpLeEVLN2gbZhRBCCCGESCtV5mhPM+u6lFYIIYQQQgghhBBCCCGE+MjIQLsQ\nQgghhBBCCCGEEEIIYQIZaBdCCCGEEEIIIYQQQgghTCBztAshhBBCCCGEEEIIIYQgTuZoTzO5ol0I\nIYQQQgghhBBCCCGEMIEMtAshhBBCCCGEEEIIIYQQJpCBdiGEEEIIIYQQQgghhBDCBDJHuxBCCCGE\nEEIIIYQQQgjU9A5gxeSKdiGEEEIIIYQQQgghhBDCBDLQLoQQQgghhBBCCCGEEEKYQAbahRBCCCGE\nEEIIIYQQQggTyBztQgghhBBCCCGEEEIIIYhTlfSOYLXkivaPXGWnSiz3XcpKv+V886N7sve/qlqG\nRZ5/sO+GJ7Wb1jJ6r9fIHizdv5hlB5fw04Q+Hyoy1ZyqsNFvFZuPrqVz3w7J3i9ftSwr9y3G/9YB\nnJvVNXrv6O0DrPJZyiqfpcxYMfmD5M1UqxL59ywl/97lZO+ZfBtnbelCIf+N5Nu2kHzbFpKtTWPD\ne4XPexpez/PH+A+SF6CiU0WW+C5hmd8y2v3YLtn7ZaqWYb7nfDxueFArSb34bsR3LNq/iEX7F1HH\nrc6HikwO5/JUOTqXqifmk//nlm8sl9O1Gk73NpO13JcAfNGmFpUOzDD81I3cSJbSBS2eN6dzOZz9\nZ1Hv+GyK9G3+xnJ5XKvgdnc99vF5E2R0/Iwm15bzZZ9mlo5qUKpuOcYdmMN433k07NMi2fv1ejRj\njM/vjPKaQb+1Y8jh+DkAxaqXZoTndMPP3NA1lGtY+YNkLlK3LP0OzOAX31nU7uOW7P1KHevz095p\n9PGcQo/NY8lZxBGAwrXK0Hv3JH7aO43euydRqHqpD5I3n1NZvvWdQUe/WVT4MXne0p3q0d5nKu57\nJ9Nq6xg+LeoAQNGWNXDfO9nw0+fmKj4rlf+DZM7lXJZGfjNofGwWxfsmz5zAsVkV2kau5dNyhQD4\ntPyXNPCZov/ZPwWHJpVMztKooRMXLxwhJNifoUN+Sva+nZ0d69YuIiTYn2P+uylQIK/hvWFD+xIS\n7M/FC0do6FL3vdb568RhBF/04/w5X/r+9B0AdetU59GDSwSc8ibglDejR/U3+e+yrVAF+z9WY79w\nLZ+0Tt7uAdjVcMZ+3kqyzV1B5gFjANDkzEW2mYvJ9vtSss1dQYZGbz7WmEPdejU5+PcuDp/yoM8v\n3yXPaGfLH0unc/iUBzu815I3n77+tmzbFE/fTYafGw8CKVWmOACuLRux98gWfI5uY8S4AWbNW69+\nbY4H7OXkWW/6DeiVYt4ly2dz8qw3ew9sIl9+/fEhX35Hbt0N4pDfDg757WDG7AmGZXZ4rOJ4wF7D\ne59/nsOsmd/X6Cm/U6fZN7Ts1DtdPv9NqjtVYYvfGrYdXUfXvh2TvV+hajlW71vK8VsHqZekDweQ\nOUsm9pzeypDJpu9X76NA3bJ0OTSDrkdmUSmFY/JXnerR0XsqHbwm027rGHLEH5MBPi+RD/ft4+i0\nfxodvaeizWD7QTLXcK7KNr917Dy2gW59OyV7/+tq5VjrvYyTt32p38zJ8HqevLlYu28Z632Ws9l3\nNW26JG/nLcXa+spl61ZgxsH5zDq8ALc+rZK936SnG7/tn8uUvb8zYt14PnPMafR+xiwZmff3ErpM\n7PlB8gLkcC5H1aNzqHZiHgV+fvO/bU7XqtS7t8nQT87VphaVD0w3/DhHbiBL6QIWz1vJqSJLfZew\n3G8Z7m84F/nDcz6eKZyL9BjxHf/bv4j/7V9E3Q94LlK6bnkmHZjLFN/5NOmT/FzEpYcrE31mM95r\nFoPWjjP0kwFyOHzOgFVj+HX/HCb6zOazvDmTLW8JNZyrst1/PTuPb6T7G44X67z/4tSdwzRwdTK8\nnnC82LB/BVsOr6Ftlzefe6VFw4ZOXLhwhEvB/gx5Q39u7dpFXAr252iS/tzQoX25FOzPhQtHcEnU\nn7ty+QRnz+wn4JQ3J457Gl5v08aVwMCDvHxxm4pflzVLfuf6tTga4MWJs/v4+Q39i8XLf+fE2X14\nHdho1L8IuxvIAb/tHPDbzvTZ4w3LtGrTDN9juzh0dCfrty4hR47sZsmawMWlLufOHeLixSMMHvxj\nCpntWL16ARcvHuHIkZ2GbZ4jR3b27dvAw4eXmD17otEy7u7NCQjw5tSpfezatYrPPvvUrJkTZKxR\nCcedf5F39wrsv2uf7P0szRuS/9BmHDb+icPGP8nSqonhPW3unOT+cxqO25fhuG0pNg65LJJRCHOy\nuivaFUXRAecTvdRSVdWwt5QPAyqpqvpQUZRnqqpmURSlIHAJCAUU4DnQXVXV0LespyBQQ1XVdfG/\nd4tfb18T/py30mg0/DzpJ4Z1GMGDyIcs8JjPMZ8T3Lpyy1DmfvgDpg+chfsPbY2WLVWxFKUrleb7\nhvoTuDnbZlGuWlmCTpyzVFxD5sFTfqHfN4O5H/mA5Z5/4rfvKGFXbhrK3Au/z6/9p9Ghd/KD7MsX\nr+ji8uE6tWg05Bz9E+E9RxB77yH5Ns7n+aETxFy7ZVTsH68jPJy8INni6stX3G6dvKGzJI1Gw0+T\nfmJkh5E8jHzIXI+5/O3zd5J6cZ9ZA2fR5oc2RstWrleZwmUK81Ojn7C1s2X6lukEHArg32f/Wjo0\nRaf1IMj9V15GPKbivqk83BfAv5fvGBXTZv4Ex55NeHr68uu/Zas/97f6A5C5ZH7KrBzKs4thFs6r\n8NXU7pxwn8J/kY+ovXcyd71P8+xyeLK8hXo0Jur0lWSrKD2hM/cPBlo2ZyKKRqH9xB7M6zSJ6LuP\nGLZrKud8Arh79XXmO8FhTHMbTsyLV9Tu5EKrEZ1Y1ncOl49fZGrToQBkss/MhMPzCT4S9EEyu07s\nxspOU3l69zE/7PqVEJ8zPEiU+fzOYwSsPQBA8QZf03hMR1Z3nc7zqH9Y22Mm/9yP5otieemyahgz\nq/1s8bx1JnVld4dpPIt8TFuPiYT5nCbqSoShzOUdx7m45iAABV2+pubYTnh0ns6VHce4suMYADlK\n5KXJ0oE8Cr6V4ueYlUahwpRu+LWfyr+Rj6nv9SsR3mf4J0ldtsn8CUV6NuLR6auG156G3uFA49Go\nujg++SI7DQ5MIdL7DKouLm1RNBrmzZ1M46bfcudOJCeOe7Lbw5tLl17vP991/5aoqCeUKFULd/fm\nTJ0yig4d+1CyZFHc3VtQtnw9HBxysc9rAyVL1wZ44zq7dnEnb14HSpepg6qq5Mz5meFz/P1P0qJV\n1zT9HSn8YWT6vj//jB9E3KMHZJv+P16dPErcndftniaPI5+06cjTET+hPn+GYq8/+YqLesTT4T9B\nbAx8khH7uct5dfIoatQj82Qziqnh1+kj6djme+5G3GPX/vXs3+vLldDrhjLtO7XmSfRT6lZ2xa1V\nY4aP60/fnkPZscWTHVv0J73FSxZl6Zq5BF8IJfun9oycMBDXet/w+FEUsxZMomadqhw98rdZ8k6b\nNZZ2LbsTEX4P70Nb2Ot5kMuh1wxlOnZpR3T0U6pUaEjLNk0ZO2EwvbrrB/vDbtzCuXbKAwu9ew0m\n6OwFkzOaomVTFzq0ac7IX2ema47ENBoNQ6cMoO83A7kX+YCVnos5ss+fG4n6cHfD7zGh/xQ69f4m\nxXX0HtqTMyc+TNunaBScJnVle0f9Mfmb3RO57nOax4mOyaE7jnM+/phcyOVrao/pxM4u01G0GhrN\n7cO+/n/y8NItPsmehbiYWItn1mg0DJsykB/bD+Be5H3WeC3lsLc/Ny6HGcpE3rnH+F+m0LnPt0bL\nPrj3iG5uvYl5FUPGTBnZ7LuKw/v8eXjP/MeLJKGtqq+saDR0/bUX0zpO4PHdR0zcNZ3T+08RceV1\nnzPs4g3GuA7h1YtX1O/UiG9HdOGPvrMM77cd9C0hf1/8YJnRKBSf1oOz7pN4GfGISvum8mBfAP+m\n0O/M17MJTxL1k+9t9eeeoZ+cj7Irh/Ls4k0sKeFcZET8uch8j7mcSHIu8iD+XKRtknORKvUqU6RM\nYfrEn4vM3DKdUx/gXETRaOg4sSe/d5pI1N3HjN41jUCfACKvvq4Xt4JvMMltGK9evMKpU0PajejM\n//rOBqDH7z+z54+tBPufI0OmT1Dj0tYXSg2NRsPwqYPo496fe5H3WbtXf7y4nvh4EX6Pcb9MpsuP\nbz9ebDm8msP7/Hlw76FZcs2bO5kmifpeHin056KjnlAyvj83ZcooOsb359q7t6BcfH9ur9cGSpWu\nTVz89mzg0o5Hj6KMPu/ixRDc3XuxcME0k7Mn5J82ayzuLb8jIvwe+w5tZl+S/kWHLm2Jjn5KtQqN\naNmmKWMmDOL77gMBuHnjFvVrG3+Bp9VqmfTbSGpXacbjx9GMmTiY777vxMxpf5gt89y5k2jWrCN3\n7kRy9OhuPDx8CAl5vc27dWtPdPQTSpeuQ7t2bkyaNILOnX/ixYuXTJgwi1KlilO6dDGjzDNnjqdC\nhfo8ehTF5Mkj6dOnG5MmzTZL5kTh+Wzkz9z9YRix9x7isO4P/vU9Tsx14zbkufdhHk1Nvr1yThpG\n9NJ1vDhxBiXjJ6Cq5s0nhAVY4xXt/6mqWj7RT1ga13MtfvlywEpg5DvKFwRSvkzNQoqXL05EWASR\nt+4SGxOL7y5fajasblTm3p173Ai5QZxq3NirqopdBjts7GywtbNFa2tD1EPjRssSSlUowZ2wcCJu\nRRIbE4vPzoPUaVTTqEzknbtcvXQdNS79D5KffFWcmFsRxN65CzGxPPPyJUu96u9eMB0VK1+MiLAI\n7sbXi8O7DlOtYTWjMvfv3CcsJAw1SUOUv2h+zv99njhdHC//e8mN4BtUdKpo8czZvi7Cfzfu8uLm\nfdSYWO7vOMrnjZNfHVto+DfcXrCTuBcxKa7ni1Y1ub/9qKXj8mmFIjy/cZd/b91HjdERseM4uRsl\nz1timDtXF+5G99I4b+7GlXh+6z7/hN5JtoylFCxfhAc37/Lo9n10MTpO7z6W7Kr0y8cvEvPiFQA3\nzl4he+7kV3JWaFqNi75nDeUsKW/5wjy+eY+o2w/Qxeg4v/sEJRoa18eXz/4z/L9dpgwQX6XvXrzJ\nP/ejAbh/+Q42GWzR2ln2u+MvyhfmSdg9nt56QFyMjqu7TlAoSd6YRHltMmVItg8CFG1Rg6u7jls0\na4IcFQrzLOwez289QI3RcXvnCRwaJd/nSw9ry+UFHsS9fP3vrvvvlWFQXZPB1rDt06pK5QpcuxbG\njRu3iImJYdOmnTR3a2RUprlbQ1av3gzA1q17qOdcK/71RmzatJNXr14RFnaba9fCqFK5wlvX2fuH\nLkyaPNvwb/DggWUGo2yKliQuMpy4e5EQG8sr/4PYVTG+ei+DixsvvbajPn8GgPpEX3eJjdUPsgOK\nrS0oluuWlf+6DGE3bnH7ZjgxMbHs3r4XlybORmVcmjixdcMuADx3+VCzTtVk62nepgm7tnkBkL9g\nXm5cu8nj+JNi/8MnaOLWwCx5v65YlrDrN7kZdoeYmBh2bNvD/2PvvMOiOt6/fZ9dFiwUacJSFBV7\nw97F3rFEo0msiSZGQ0w0URNriiUxlsQYjcYkajT2jqigAoq9IHaxgAq7gDQ7Asu+fywsLItGlIUv\nv3fu6+K63HNmDh/G2ZlnnvPMM917djQo071HBzb+ux2A3Tv208b7f3v+zk1jr7rYWFsVtwwDajeo\nyb2oGGL0NtxBvLsa9uWX2XA16lbDztGWkyGni0SvU54xOWL3CSrnGZPTco3JitIW+gV6xbZ1Sbh6\nj4SruoV+asrjIrFL6zSoSXRUNDF3VWSkZ7B/5wHa5dPGN67e0juesslIzyA9TTdemFsokGRFs4wr\nabZyFS9P4qLU3L8XhyY9gxO7Q2nUualBmavHL5GWZefcDIvATpnzItajTmWsHcpxsQgCDrKxbujJ\nU72drCF+xzEcuxnvLKz81SDu/LbrhXayU7/WxBWBnVw9z1okeFcILfKsReKi44m8FkVmPmuRC7nW\nIrevRNK4CNYilbw8ib8TS8K9eDTpGZzafRSvPHby9eOX9f3iVtgNbJ11/ULp6YZMLuNKqC5g7fnT\nVH05U1KnQU3uReYaL3YcpF3XNgZl1PeyxwvDdjYaL6TCSwGR1/bauGknPnnsOZ8X2HM+Pl3ZmI89\n9zKuXbtJRMStl5YpCA0b1SPy9t1c9oU/3fLYF916dGTTvzsAnX3R+j/sC0mSQJIoU7YMAFZWlsTF\nxhea5iZNvAzafPPm3fj4dDEo4+PThbVrtwCwbZs/7dvrfDBPnz7j2LHTPH+eaqRZkiTKZmm2trZE\nrY4rNM3ZWNSpTvo9FRkxsZCRwZN9wZRp1/KV6ioqV0Ayk5N64hwA2mepaFOfF7pGQf5otVKJ/yku\nSqKj3QhJkkZIkrQk12c/SZLaFeAR1kByVl0PSZKOSJJ0LusnexT4AWgjSdJ5SZKy90a7SJK0T5Kk\nG5IkzSuMvyU3Ds72xKvu6z/fVydg7+zwkho5XD13lfPHw9l0Zj2bzq7nTMhZ7t68V9gSjXB0djTQ\nHK++j6Py1bfWmVuY8/fe5azcvZS23Vr/d4U3RO5kT3psjt6M2ATk5Y3b2LJLK9y3L8N50TTMnHP+\nHsncHLdNv+K2/mfKdiyaRYeDswP3c7VxgjoBe2f7l9TIIfJqJI3bNcailAXWttbUa1EPRxfTb320\ncLbjuSrHyfVclYRFHs2WdTywcLEnMfDcC59Tvk9L4reHmkxnNqWUtjzLpTdVnUgppeFWOus6HpR2\nsSM+MMzguryMBVV8fYiYv9XkOnNTzsmO5Fyak9WJ2Di9OCVCy4EduBxsHHXY2KcVZ3aZfpEGYOVk\nx4Ncmh+qk7B2Mt6y2HRoZz4PWUiXr95lzzerje7X6t4U9eU7aNJMG41Y1tmWx6ok/efH6iTKOhvr\nrTO8E4NDF9ByyjuEzlhjdN/Tpxk3dhaNo720sx3PYnLa+Jk6idJ5NJerU5HSLvaoD4TlrY5dgyp0\nDv6RLkE/cG7yX68dzQ7g4urMveicSNPoGDUuLs4vLKPRaHjw4CH29ra4uORT19X5pc+sXNmDgW/3\n1kVa7foHT89K+nLNmzfi7JlA/Hb9Q61aOdE9r4Nk54AmIWcxlZl4H5m94Twid3FD5uKO1ZwlWP+w\nFEWDHKePzN4R60V/Ue6PzaRu/9ck0ewAzkon1DE5Cyi1Kg5nZXmjMiqVroxGo+HRw8fY5tn67NO3\nKzu36hztUbfvUqVqJdzcXZDL5XTt0QFlnv/T10Xp4kRMTKz+syomDqXScLuws9KJmBi1Xu/Dh4+w\ns9P17woV3Th0ZDs79/xD8xaGTpzFv80h6MgOJkws2h1p/+s4OjsQp8rpy3EFsOEkSeLzmZ+w+Ptl\nppJnhKWzLY/yjMmW+cwh9YZ1YviRBbSe8g4hM3VjcrnKzmjR0vefSby7ZxaNPi6aNG+Ozo7ExuS0\ncbz6PuWdX90Oc3Ipz8aDq/A/u43VS9aZPpqdkmcr2zrbk6TOaZckdSK2+QQWZOM9qCPhwTrbU5Ik\nBk8bwfo5xraGKTG2kxOxyKNZZyc7vNROdurTokgc7fb5rEUcXnEtcvtqJE1yrUXqF9FaxNbJjmRV\nTjR3sjoR25fYyW0GduBisM4ucqqs5OnDp4z9fSIz9vzEgK+HFsmLrvJKxzxjcnyB1tVOLuXZeGg1\ne89uZ9Vv6wolmh10tlp0LtsrJkaN6yvac64uxnVdXHV1tVote/3Xc/LEXkaNNE5dVlg4uzihyrId\nAFQxsTjnsS+UyvIG9sWjh4/0qWAqVHTjwJFtbN/zD82y7IuMjAwmT/iW4GO7uHD9MNWqV2Hdmi2F\nptklv3bLk0Ild5lsm+hlqWAyMjIYN24qZ84EEBl5hpo1q/L33xsKTXM28vIOaHLNIZr4BMycjOeQ\nMh1b47p5OeXnT0fupOvniopuZD56TPmFM3HZuAzb8R9CEb1kFgjehJLYS0tnObvPS5K0/Q2eUyXr\nGbeACcDCrOvxQGetVtsQGAQszrr+FXAkKwo+ez+NV1aZusAgSZLc8/4SSZI+kiTpjCRJZ2IeFyy6\nNd83z6+4VcbFw4WKnu6803Qwg5q8R4OW9anbrE6Bfv/rkO/L8gJs7+nbZCDvdx/NjE++Z/y3vrhW\ndPnvSm9C/oINPj0JOkFUp+Hc6zeGpyfCKD/nS/29qI5DiB74KbETf8Dhq48xc1eaVi/okh3l5RWb\n+Nzhc5wJOsOCHQuYvGQy185dQ5OhKVR5+ZJfV84tWpLw/G4Et74xdkpmY9XQE82zNJ5cM/0Lo3z7\nhdbwfu3vhnL527VGxapPHMDtFXvRPC3it+0FGC+a9m1DxXqVObBil8F1a8dyuFSvUCRpY+BFko01\nn/onkJ+9JxDwwwa88+T3d6zqSpev3mHXlD9NJVNPfmNyfk18afUB1rX+guNzN9BonKHe8l5VyHiW\nRlJR7Xb4rzFZkqj/7RAufLMu3+pJYbcIbDeZg92nU+PT3rrI9teVkm/7aV+hzIvrvuyZFhbmpKY+\np3mLHqz8619WrtClBTgXdpHKnk1p1Lgzvy39m62b/3qtvyeXaONrefuFXI5c6caj6Z/xeOF3lB07\nEamMJaBzzD8c/wEpY97Don03JBvT5MfMdxw2av+Xl/FqVJdnz1KJuKZLMfTwwSOmfjmLJX/+xJY9\nq4i+G0OGpnBeeL1+f9ESFxtPg9rt6dCmH9On/sDvKxdgaVUW0KWN8W7Zm17dB9O8ZSMGqBYmQAAA\nIABJREFUvlN0ea7/13mVNn8RA0b04+ihEwZOIZPzimPyhTUHWN3mC47O3UCTrDFZJpfj0rga+8Yt\nZXP/76jStTHurWqbWvEbtTFAnCqeQR1H0KfFIHoN7Iadg4nGi9yUMFs53/i1FzRxq35tqVzXkz3L\ndZGrnYZ143zQOQNHfZGQX7/IYydX/W44N19iJ1sXoZ38BktUzh0+x+mgMyzasYCvl0zmapGtRV79\nu9e8bxsq1qvC/hU7AV2KjapNarBp9mpm9Z6MYwUnWg1oZ0q1Ot6kockaLzoMp0+LQfgM7F5o44Up\n7DkA73Z9adqsG718hjBmzAhatzbeVVcYvJK/4gX642LjaVi7A53avMXMqT+wbOV8LK3KYmZmxoiR\n79CxbT/qVW/LlcsRfDbho0LU/Po20YswMzPjo4+G0rx5DypVaszFi1eZNMk43/4b8wq6noYc5173\nocS8PZpnJ8NwnDVRd0Mup1SDuiQtWI7qvU9QuCmx7NPF6HkCwf8aJdHRnjt1jPHpNq9OduqYKsDn\nwIqs6wrgD0mSLgKbgZedsHdQq9U+0Gq1qcAVwOjkGa1Wu0Kr1TbWarWNXS3djJ/wEu6rEyif6w2/\no9KBxFeMXGndtSVXwq6R+jSV1KepnAo6Q80GNQv0+1+HePV9A83llY7cj331t+fZkTmqu2rOHTtP\ntTpVC11jbjSxCShyRd2YOTugiTds48wHjyBdt/Xu4ea9WNTO0aS5r4ukyoiO5dmpC1jUrGJSvaCL\nGskd+eFQgH4BsOHXDfh282Xq4KkggSpS9d+V3pDn6iQsXHIiXSxc7EiLzYlCk1uWpmwNd7y2fUPz\n079h3agqddZM1h/0BFC+b6siiWYHSFUlUTqX3lJKe1Jjc1IvmVmWwrq6Oy23zaDj6cXYNvSk6eov\nsalfmXINPKk1/T06nl5M5Q+7U3VcXzw+ML1BkBKbiG0uzbZKex7EG6eLqt6qLt18+7Fs1Dwy8kSA\nN+rVgvD9p8gsigUP8DA2CZtcmq2Vdvp0MPlxafdxanbOSeFj7WzHu8vHs23C7yTfNb1z57E6CUuX\nnOgnS6UdT+NenJLrxs4TVMqTpqVqn+ZFFs0OWRHsrjltXFppx7O4nDY2syyFdQ13vLdNo/upn7Fr\n6EnLVV/oD0TN5tENFRlPn2NTo2DzWG5iotW4u+W8PHVzVRptUc1dRi6XY2NjTVJSMjEx+dRVxb30\nmdExarZt3wPAjh17qVtXNwc+evSYJ090uWD37juEQmH2Roc/aRPvI3fIiQyX2TuSmWQ472Um3ift\nVChoNGTGx6JR3UPmYtiW2uRENHejMKtVOAd95SVWFYfSNSf6SeniRFyuCCPQRblnR0jJ5XKsrC1J\nSX6gv+/Tr5s+bUw2B/eH0LfLYPp1G8qtm1FE3SqcswdUMbG4uuZEyLm4OhGbZxu2WhWLq6tSr9fa\n2ork5BTS0tJJTtb18wvnLxMVeZcqWTsaYtW6Zzx5/IRtm/1o2Mg07V0SiVffx8klpy87KR1JeEUb\nrl6j2gx8/y12ntzIZzPG0mNAV3ynjDaVVEA3JlvlGZOf5DPvZXN91wmqZKWWeaxOIubkNVKTH5OR\nmkZUUDiOdTxMqhcgXh2Ps2tOG5dXOr5WlGlCXCK3r0fSoFn9wpSXLyXNVk6KTTRIBWOntCc5Lsmo\nXO1W9ejtO4CFo+bq7SHPhtXpPLw7i0J/572pw2nzVjsGTTY+gLKwea5OzGMn25OWy+6UW5aibA13\nGmybSYvTS7BuVJV6ayYZ2clFEc0Ob74WWf/rBsZ28+XrwVORJIgpgrVIcmwiti45UbS2SntS8hkv\naraqS0/f/iwZ9YO+XyTHJnLvShQJ9+LJ1GQSFnCKCnUqG9UtbOJV8XnG5PIFWldncz8ugVvXI2nY\nvHDGi5hoNW65bC9XVyWqV7TnomOM66qzdtJl22/37yeyY+demjTxKhS9eVHHxOHimvPCz8XVOR/7\nIs7AvrB6oX1xjyqelahTrwYAdyJ1L7p2bd9L42YvT4lTEGLyazd1/AvLZNtESUkvXlfVr69zc92+\nrTvTYetWP5o3L/w0Tpq4+8hzzSHy8i+fQx5t9ceiZrWsugk8v3ZTl3ZGk8nToGNY1DCtf0ggKAxK\noqM9PzIw/FtKFbD+LiD7yPPxQBxQH2gMmL+kXu6QVQ2FfLjs9fDruHq44uzuhJnCjHa923Es8MQr\n1Y1X3ad+s3rI5DLkZnLqNa/L3ZumP3jv6vnruFdyQ+nujJnCjM59OnAk4Ngr1bWysURhrouWtLGz\noV6TOgaHQ5mC1EvXUVR0xczVCRRmWHZvx5MgwzaWO+Qs4sq2b64/uENmbQkKnV5ZOWtKNaxNWiE5\nGF5GRHgELh4uOGX1C+/e3px4xX4hk8mwKqfLCetRw4NKNStx9vBZU8oF4FHYTUpXVlKqQnkkhRnl\n+7YiYf8Z/X3No6ccrTWSE00+4USTT3h49gaXhv3Io/CsQ/okifI+LYjfUTQLiJTztyhb2ZnSFRyR\nFHJc+rYgNiCnnTIePWN/7Y842GQcB5uMI/ncTU4Nn8+D8Nsc6/ut/vrtP/ZyY/EOov4KMLnmO+G3\nKO+hxN7NEblCTiOfllwIPGNQxq22B+/N+ZBlo+bxOPGh0TMa927Fmd1F08YAMeG3sfNwplyW5ro+\nzbkWaNgf7TxyHIPVOniRGKVLJVHKugxD/v6SA/M2cjfXoWCmJD78NjYezli5OyJTyPHs3ZzIPFu4\nbXLprdjRiwdROakvkCSq9GxWZPnZAZLP38aykjNl3HV92b1Pc9T7Dfvy7tofs7fp5+xt+jlJ525y\nbMQCksMjdXXkuqm1jJsDVlWUPLl3/0W/6j85feY8np6V8PBwR6FQMHBgH3b7GX43dvsFMHTo2wD0\n79+ToOCj+usDB/bB3NwcDw93PD0rcep02EufuWvXPtq30+Wn9G7bgogbuvHEySnH2G/S2AuZTGZ0\n8FZByLhxDZnSDVl5ZzAzw7x1B9JPG36P0k+GoqirW3BJVjbIXNzJjFMh2TuCuc7MkMpaYlazDpkx\npolGDA+7TKXKFXGv4IpCYYZPv24E7g02KHNgXzD93+kNQI/enTl25JT+niRJ9OzTxcjRbp81R1rb\nWDH0g0FsWLutUPSGnbtIpSoeVKjohkKhoO9bPdnnf8igzD7/Qwx6Txdz4dO3K6GHdXOhvb0tsqyt\nxRU93KhcxYM7UfeQy+X61DJmZmZ06dbO4PC2/9+5cv4aFSq54eKuzLLhOnI44NXmhOm+3+PT5G36\nNBvEL98txX/LfpbMWW5SvXHhtylXyRnrrDG5mk9zbucZk8vlGpMrdfQiJWtMvnP4Ag41KmBWyhxJ\nLsO1eQ2SbhgePGkKLp+/hnsld30bd+3TiZD9r9bG5ZWOWJTSjRdWNlbUb1KPO0Vgd5Y0W/l2+E2c\nKylxdC+PXGFGc5/WnAs0PDegYu1KfDD3YxaOnMvDxJyXics++5nPW45mfOuP+Xf2ao5sC2bjj8a7\nFwubR2G3KFNZSaksu7N835Z57ORnhNYaxfEmvhxv4svDsze4MGxeHju5OXFFZCdfD4/ANddapN1r\nrkUqFeFaJCr8Jk4eShzcdP2iqU8rwvP0C/falRg6ZzS/jvqBR7ns5MjwW5SxKYulnTUANVvWQX3D\n9DsTL5+/RoXKbrhUyBov+nYkOODVgo7yjhdeTeoSVUi+gLy216CBffDLY8/5vcCe8/MLYFA+9lyZ\nMqWxtNTtPCtTpjSdO3lz+fL1QtGbl7BzF6lcpSIVKrpm2Rc92J/Hvtjvf4iB7+l2QL3cvqjInah7\nqFXxVKteRR+44d2+pcFh82/KmTPhBm3+9ts++PkFGpTx8wtkyJABALz1Vg+Cg1/ug1Gp4qhRoyoO\nWeN3x45tuJa1Y7EweX75OooKrpi56uzkst3a8TTEcC2Uew4p064FaZF39XVl1pbIbG0AKNXUi7Tb\npj3sWZBD5v+Bn+LCtCfHFR1RwFhJkmSAK9D05cWNaA1kn7BhA0RrtdpMSZKGA/Ks64+AIj21KlOT\nya/Tf+OHtXOQyWXs2xjAnYg7DP9iGBEXIjgeeILq9avxzR8zsLSxokWn5gyfMIxRnT7i8J4jeLWs\nzx+By0Gr5XTIGU4cOGlyzRqNhvlTf+GXf39CJpfht2EvkRFRfDjxfa6FX+dIwDFq1q/Oj3/Owqqc\nJa07t+DDL0fwXvv38ahakck/foE2MxNJJmPNb/8SdcPEA6kmk/uzf8PljzlIMhkPtweQdvMOdr7D\nSL0cwdOgE5Qb2ocy7VtAhgbNg0fETdGlHzCvXAHHb8ZBphZkEsl/bCS9CBY8mZpMlk1fxqy1s5DL\n5QRsDOBuxF2GfjGUiAsRnAw8SbX61Zj+x3QsbSxp1qkZQyYM4eNOHyNXyJm/dT4ATx8/5adxP5H5\nBjmXXxWtJpMbX/9JvQ1TkeQy1OuDeHo9Go9Jg3gUfovE/WdeWr9ci5o8VyeSeqdotqRrNZlcmrKK\n5uu/RpLLuLc+mMfXo6k+aQAp5yOJCzD9gqCgZGoy2TjjL3zXTEUml3F8UxDqG9H0Gj+QOxdvcfHA\nWd76eggWZUoxaukEAJJjEvj9Q93xEnZujtgqHbhx4kqRat4zYxXD1kxGJpdxblMI92/E0GF8f2Iu\nRnL9wDmaDe9ClVZ10GRoSH3whG1f/A5As2FdsKvohPe4fniP0zna1gz9gSf5vEAoLLSaTI5MX43P\n2klIchnXNoaQHBFDky/6c/9CJFGB56g7ogturWuTmaHh+YMnHByf42hyaVaDx+okHt59fWf162g+\nP2UVbdZPRpLLiNoQwsOIGGpN7E9yeCTqgBfnenVoVp3qvj5o0zVotZmEff03aUmPX1uLRqPhs8+n\n4b/nX+QyGatWb+TKlQi+mfklZ86G4+cXyF9/b2D1qsVcuxJKcnIK7w3R5dC+ciWCLVt2czE8iAyN\nhnGfTdUfFJjfMwF+nPcb/6xewmeffciTx08Z/bFuG2r/t3oyevQwMjI0pD5LZfCQN8zTnanh6R8/\nYzVzPshkPD/oj+ZeFKXf/YCMm9dIP32M9LBTKLyaYLN4NdrMTJ6tXob20UPM6jemzIix2fupSd2x\nEc3dwluY5Uaj0TBj8hzWbF6GXC5n0787uHH9FhO+GsuF81c4sC+YjWu3s2jZHEJO+5GS8gDfUZP0\n9Zu1bIRaFce9O4bOyJlzJlOrji7y6JeflhN5q3DmbY1Gw9dffsembSuRyeWsX7uV69duMnnKOM6H\nXWL/3kOs+2cLS1f8xKmwAJKTH/DRB7ojdFq0asLkKePIyNCQmanhy/EzSUl+QJkypdm0fSVmZgrk\nchmHg4/zz6pNhaK3oEyc+QOnwy6QkvKQjn2HMHbkUPrnOUyuqNFoNMyb+jOL/52PXC5j1wZ/bkdE\nMXriB1wNv87hgKPUql+DeX/OwrqcFa07t2T0lx8wqP3wYtGr1WQSPH01ff/RjclXNoaQFBFD8wn9\nibsYSWTgOeqN6EKF1rXJTNfNIQETdGPy8wdPObdyL+/4fYdWqyUqKJyoQ8bnlhQ2Go2GH6cs5Lf1\nC5HJZezasIfbEZF8PHEkV8Kv6dt4wV9zsC5nRdvOrfh44kjebjeUSlUrMmGmb/ZwwT+/r+fmNdOM\nF4aiS5atnKnJZPWMlUxaMwOZXEbIpoPE3LhH/wnvEHnhFucOnObdKcMoVaYU45bqUtwkqhJYOGqu\nSXW9DK0mk4iv/8Iry05WrQ/iyfVoKk0ayKPwWyTsf7ndWdR2cqYmk9+mL2PO2lnIstYidyLuMixr\nLXIiay0y44/pWNlY0rxTM4ZNGMJHWWuRBbnWIj8W0VokU5PJvzNW8vmaacjkMo5uOoTqRjR9xg8i\n6uItwg+c4e2vh1KqTCk+XvoFAEkxCSz58Ee0mZlsnr2GL9fNBAnuXLrN4Q0HTK5ZN14sYun6hcjk\ncnau9+P29UjGTBrFlfPXCAkIpZZXDRb+NTfXeDGKAd5DqFTVgwnf+OrtizXLCm+8yLbn9uSxvWbO\n/JKzuey5VasWczXLnhucy57bvGU3F/LYc05OjmzZrEsDKTeTs2HDDgICggHo06cbPy+ahaOjHTt3\nriE8/DI9e71+DnedffE9G7b9iVwu09sXk6Z8SnjYJfbvDeLff7awZMU8ToTtJyX5AaM/0K2dmrdq\nwqQpn6LJ0KDJ1DBp/Df6nX/zf/yNHXvXkpGeQfQ9FePGfP0GrWys+fPPp7N79z/I5XJWr97I1asR\nzJgxgbNnL7JnTyCrVm3kr79+5vLlwyQlpTBsmK++/vXrR7GyssLcXIGPT1d69RrCtWs3mD37Zw4c\n2Ex6egZ378bw4YcTCk1zjvhMEucuwXnZXJDJeLRjP+m37lBu7HDSLkfwNOQ41u/1pUy7FmgzNGQ+\nfETC9J90dTMzSVq4AuWKeSBJPL9yg0db/Qtfo0BQyEgFyQv4v4AkSY+1Wq1lnmsSsBZdzvRLgBPw\njVarDZYkKQporNVqE7LrSpLkAVwFrqNL5ZcG+Gq12pOSJFUFtgJPgSDg06w6CmAf4ACsQnd4amOt\nVuubpcEPmK/VaoNfpL2Te9cS1diPNSXvROe15SyKW0KB+fRRydpYMjnN8r8L/Y/xSCp57xT3liqa\n9C2FhQOvn7u7uHDWFN9J5K9D+YwSNYUA8E5icHFLKDDxPT2LW0KBaHD49aPxi4unGSXPvlDd2vvf\nhf7HaFlvRHFLKBBDzCoUt4QCszo9qrglFIhNtmWLW0KB+eZJydL8QWrJszl/NH9U3BIKjLu8ZPWL\ns6nq/y70P8alpKjillBg7EoXaVzkG/Pg+dPillBgrlavXtwSCkyl8MCStej7H+GI84CSt/jMQ5vY\nLcXyf1/iLIG8Tvasa1og39eaWq3WI29drVYbBZR+QfkbQO6EnV9nXU8HOuYpvipXvV6vIF8gEAgE\nAoFAIBAIBAKBQCAQCAT/xyhxjnaBQCAQCAQCgUAgEAgEAoFAIBAUPlrERoDXpWTlrBAIBAKBQCAQ\nCAQCgUAgEAgEAoHgfwzhaBcIBAKBQCAQCAQCgUAgEAgEAoHgDRCOdoFAIBAIBAKBQCAQCAQCgUAg\nEAjeAJGjXSAQCAQCgUAgEAgEAoFAIBAIBGRqi1tByUVEtAsEAoFAIBAIBAKBQCAQCAQCgUDwBghH\nu0AgEAgEAoFAIBAIBAKBQCAQCARvgHC0CwQCgUAgEAgEAoFAIBAIBAKBQPAGiBztAoFAIBAIBAKB\nQCAQCAQCgUAgIBOpuCWUWEREu0AgEAgEAoFAIBAIBAKBQCAQCARvgHC0CwQCgUAgEAgEAoFAIBAI\nBAKBQPAGiNQxRcj5B5HFLaFAlFWUKm4JBWbh0xrFLaHAhD26WNwSCsSfNjWLW0KBict8VNwSCox5\npry4JRSIbQ9vF7eEAuNS2r64JRSIfbVKVp8AsDiuKG4JBSb4uGtxSygQyan3iltCgUnNSCtuCf9f\ncOzCquKWUGCGN/qiuCUUCPWzpOKWUCC2mXsUt4QC80T7oLglFIilpTKKW0KBiU4tWf0YQG5RslIa\nPMx4VtwSCoyTpW1xSygwcY+Ti1tCgZCkktWPAX59ULLWTwALi1tACUUrUse8NsLRLhAIBAKBQCAQ\nvISW9UYUt4QCURKd7AKBQCAQCAQCQUlHpI4RCAQCgUAgEAgEAoFAIBAIBAKB4A0QjnaBQCAQCAQC\ngUAgEAgEAoFAIBAI3gCROkYgEAgEAoFAIBAIBAKBQCAQCARkFreAEoyIaBcIBAKBQCAQCAQCgUAg\nEAgEAoHgDRCOdoFAIBAIBAKBQCAQCAQCgUAgEAjeAOFoFwgEAoFAIBAIBAKBQCAQCAQCgeANEDna\nBQKBQCAQCAQCgUAgEAgEAoFAgBapuCWUWEREu0AgEAgEAoFAIBAIBAKBQCAQCARvgHC0CwQCgUAg\nEAgEAoFAIBAIBAKBQPAGCEe7QCAQCAQCgUAgEAgEAoFAIBAIBG+AyNEuEAgEAoFAIBAIBAKBQCAQ\nCAQCMotbQAlGRLQLBAKBQCAQCAQCgUAgEAgEAoFA8AYIR/v/OB06teHE2X2cOh/IuPEfGd03N1ew\n8u+fOXU+kP2HNuNewRUA9wqu3Iu7QFDoToJCdzJ/0bcm1endoRWHTu4i5LQfYz77IF+dS1bOI+S0\nHzsC1uHm7gJA3wE98A/epP+JvH+eWnWqA9Crb1f2Hd5C4NFtfD1zvMm01/Kuz8yDP/NN8GK6jOlj\ndL/DyJ5MD1zI1L0/MW7ddOxcHQCo1qI2X/vP0//8cn0t9bs0MZnO3LTv2JrQ0/4cP7cP389HGd03\nN1ew/K+FHD+3D/8DG3Cv4KK/V7N2NfwC1hNyfDdBR3diYWFeJJrrejdg3qFfmR/yG73G9DO6322U\nDz8c+IXZ+xby1b/fYO/qaHC/lGVpfjn5B8O+M/57TUHjdo34M3glfx/5i0FjBxrdr9usDr/5L2Fv\n5B7a9GhtcG/UlJGsOLCclYdWMPbbMUWiNy8NvRvxe9ByVhz+gwFj3za633dUX5YeXMav+5cwe/1s\nHPO0t6ko7L5bunQp1m78nSOn9hByfDdTZ04wqf6W7Zux7ci/7Dy2gRG+Q4zuN2xen3UBf3LqXjAd\ne7bTX1e6ObFu/5+sD/ybzcH/0H+Y8VhjKsybNMVu1T/YrVlHmXfey7eMhXd77P5ajd2fq7CeMl1/\n3WbuPBx2+mEze65JNXbu7E3Y+YNcuBjMF18Yf2fMzc1ZvWYJFy4GExyygwoV3ADo0KE1oUd3c+rU\nPkKP7sbbuwUAlpZlOX7CX/9z5+455s2bYTL9Tu3r0Tl0Pl2OL6Sar88Ly7n0aspbsf9Srn4lAGwb\nVKHDgTm6n4Nzcene2GQaATp1bsvZsAOcv3CI8V98bHTf3Nycv1cv5vyFQxwK3kaFLJuiUaN6hB73\nI/S4H0dP7KGXTxcALCzMCQrZztETezh5eh9Tpn5uMu1durTj0sUQrlwJZeKXn+Srfd3apVy5Ekro\nkd1UrKjrI3Z25QjYv4mkxOv8/PMsk+nLjxbtmrLlyFq2Hf2X4b6Dje43aFaff/av5PjdQ3To6W10\nv6xlGfac3crE2aZr14Iwbc5C2vZ8h75DjPtOcVHPuwHzDy1hYchSfMa8ZXS/x6jezDuwmB/2LWLK\nv9/ikDXXObg6MttvPnP8FzIv8Bc6Du5qUp3tO7bmyOk9HHvJ3Pf7Xws4dm4few5swC3P3Lc74F+C\nj+/i0NEdertNoVDw08/fEHrGnyOn/OjZu7PJ9FfyrseoQz/xYcgCmo0xHuO8Bnfg/f1zGe4/m/e2\nTMe+qk6/tZsD46//xXD/2Qz3n02X2e+bTGNuGng3ZEnQMpYeXs5bYwcY3e89qg+LD/7Gov2L+Xb9\nLAMbaPqab1h7cT1T/zbdnJEfXt4N+eXQUn4NWU7fMf2N7vca1YdFB5awYN9iZv77vb4ve9SqxOzt\n81gUqLvXsldro7qmoHX75vgd3cTeE1sY9ekwo/uNmnuxOXA14TFH6dKrg8G95et/5njEAX5bu6BI\ntOo1eTdiRdAKVh5eydv52MZ1mtZh8Z7F7L69m1Y9Whnce//r91kauJSlgUtp69PWpDrbdmhJ4Ilt\nHDq1k9HjRhjdNzdXsHjlDxw6tZOt+1fj6q4EQKEw48fF3+B/eCN+wRto1qoRAKVKl2Ll+l8IOL6V\nvaGbmTj900LV265jK0JO7ib0jD+ffDYyX71L/5xP6Bl/dgf+q/cD9BvQk/0hW/Q/dxMu6P0Aazf/\nTsDhrRw8toO5C2Ygk72526pLl3ZcunSYq1dCmTjxBXbEumVcvRLK0dAcOwJg0iRfrl4J5dKlw3Tu\nrJuvq1WrwpnTAfqfxIRrjPtUN75Pnz6BqMgz+nvdunUw+n1v/LeUIJuohnd9vjq4kCnBP9NhTG+j\n+94jezApcD5f7v2Rj9dNwzbL3wLQ66v3mLj/Jybu/wmvXi2KTLNA8CaUmNQxkiQ91mq1lrk+jwAa\na7Va35fU0ZeRJMkR8APMgXHAP8AjQAPIgWlarXbnf2iYotVq52T92wPw02q1dd7gz3opMpmMHxfM\nZECf91HFxBIYvJV9/geJuH5LX2bwsLdJSXlAU6/O9Ovfk5nfTmTU+7oFWVTkXdq3Nr0zRyaT8f28\nKQzu/xGxqjh2HVjPgX3B3Lh+W19m0JC3eJDyEO8mvfDp142vZn6O76hJ7Njiz44t/gBUr1mVlWt/\n4cql65SztWHKtxPo1eEdkhKTWfDbLFq1bcbRwycLVbskkxj03UgWD5lFSmwik3fN5ULgGWJvxujL\nRF+J4gefr0hPTaPNkM70+3oIf/r+TMTxy8ztMQmAMjZl+TbkV64cDi9Uffkhk8mYO386A/uORK2K\nY1/QJgL2Bhn0i/eGDiAl5QEtGnajz1s9mPbNl4z+YAJyuZzfVszDd/Rkrly6jq1tOdLTM0yuWZLJ\nGP79h/w4+FuSYhP5btc8zh04jepGtL7MncuRzOg1kbTUNDoO6co7Xw/jN98cA3zAF+9y7eRlk2sF\nXRv7zvqEr96bQoI6gV/9FnM88AR3b9zVl4mPuc/8CQsYMNpwMVSrUU1qN67Fx110zsKF2xZQr3k9\nLpy4UCTas/WPmTWGaYOnkahOYNHuRZwMPMG9G/f0ZW5dvs34np/zPPU53Yf04P0pHzDvkx9Nrquw\n+66FhTnLlvzF0SOnUCgUbN75Fx06teHQgSMm0T95zgTGDhpPnDqetXtXEhIQSmRElL6MOjqObz6b\nw9Ax7xrUvR+XyAifj0lPS6d0mdJsDl5DyP5QEuISC11nHtFYjfuc5ElfkHn/PrZLl/P8+FE0d+7o\ni8hdXSnz7mCSx32C9vFjpHLl9PeebtqAVKoUpXu92Hn85hJlLFz0HT69hhATE8uRI7vYsyeQa9du\n6ssMHzGQlJQH1KvbjgEDfPh+1lcMH+ZLYmIyAwaMJFYdT61a1di5aw1VPZvz+PGrBItjAAAgAElE\nQVQTWjTvoa8fenQ3O3fuM9EfIFF/7vuEDpzLM3Ui7ffNQh1wjkcRMQbFzMqWwnNkV5LO3tBfe3jt\nHkFdp6HVZFKqfDk6HJqLOuAcWk3hb9aUyWQsWPgtfXyGERMTS/CRHfjvOcD1XO08bPhAUlIe4lWv\nA/0H9OLb7yfz/vBxXLkSgXfrPmg0GpycHTl2Yg97/Q/y/HkavXoM5smTp5iZmRFwYBOBAcGcPn2+\n0LX/8sssevR4j+hoNceP7cHPL4Cr13La8v333yE55QG1arVm4Nu9mTN7CoOHjCU19TnffPsTtWtX\np3btGoWq6780T5ozHt93JhCnvs9q/xUc3h9K5I2c715sTBzffj6HIR+/k+8zPp40inMnCrct34S+\nPTrzXv/eTPl+fnFLAXS2xfvff8Tcwd+QGJvIrF3zOHfgFDG5bIuoy7eZ1utL0lLT6DSkK+9+PYxf\nfReQHJ/MzLe+IiMtA4sypZgX8AtnA0+REp9c6DplMhlz5k9jUN9RqFVx7A3aaDT3vTu0Pw9SHtKy\nYTf6vNWdad98wccffIFcLmfJih/5dPRXWXOfjd5u++zL0STcT6J14x5IkoStrU2hawedrdzp++Fs\nGvwDj2KTGLbrO24eOEviDZW+zJWdxzm/7hAAnp0a0n7aELYMnwdAyp04VveYahJt+SGTyfho1sd8\nM3g6iepE5u1eyKnAk0TnsoFuX77Nlz0nkJb6nK5DujNsyvss+ESnd8fybViUtqDr4O5FqnnU96P5\nbvAMkmIT+WHXAs4cOGWgOfLybSb3mkBaahpdhnRn6NcjWOT7E8+fPefX8YuIjVJjW96OeXsWcv5w\nGE8fPjGp3qk/TOTDgZ8Sp4pn4/5VBO0/wq2ISH0ZdUwcUz/7nhFjjF8y/rV0LaVLl+LtYcaBN6bU\nPHbWWKYOnkqCOoGfd//MiTy2cbwqnoVfLKR/Htu+SYcmeNbxxLebLwpzBfM2z+N00GmePX5mEp3f\n/DiZ4QPGEquKY3vgWg7uC+FmrrZ9e3BfHqQ8pEPTPvTq14XJMz9j3KivGDRU97KxR9tB2DvY8tfG\nJfTtpAsKWfnbP5wIPYNCYcY/25bj3bElIQePFYreWfOm8d5bH6JWxbLn4EYC9gUZ+AHeyfIDtG7c\ng95vdWfKNxMYO/JLtm/Zw/YtewCoUbMqf65bzJVL1wH4+IMvePxI14dXrF5Er75d2bVt7xvpXPzL\nbLr3eJfoaDUnjvvr7IirOXbEB++/S0ryA2rWas3Agb2ZM2cqgwePoWbNqgwa2If6Xh1wcXFi394N\n1KrdhoiIWzRu0kX//DtRZ9mxM0fjL4v/YNGi5a+t+WV/S0myiSSZxFvffcDvQ2bzIDaR8bvmcDnw\nLHG5/C0xV6JY5DOF9NQ0Wg7pTK+vB/OP7y/UbN8A19oeLOgxGTNzBZ9snMHV4PM8N8F3TyAoTP5/\nimjvCFzTarUNtFpttgemvVar9QIGAItf4RlTTKYuHxo2rkfk7TvcibpHeno627fuoXvPTgZluvfs\nyIb12wHYtWMfbdoV/Vs+r4Z1iIq8y707MaSnZ7B7+z46d29vUKZz93Zs3bALAP9dgbRq28zoOb37\nd9dPoBU83Ii8dYekRN2CJzTkBN19OhnVeVM8vDy5fyeWxHvxaNI1nN19zCgqPeL4ZdJT0wCIDLtB\nOWc7o+c06NGcy8Fh+nKmpEGjekTevsvdO9Gkp6ezY6s/XXsYviXv2qMDm9br3hv57dxPa+/mALTr\n0Iorl67rjZjk5BQyM02ffauKlydxUWru34tDk57Bid2hNOrc1KDM1eOXSMtqv5thEdgp7fX3POpU\nxsahHJeK4EUGQHWv6qii1MTejSUjPYOQXSG07GL43YqLjiPyWiRardbgulYL5hbmmJmboTBXYKaQ\nk5xQ+Av3l1HNqxrqKBVxWfoP7z5M8y7NDcpcPH6B56nPAbgedg0HpUN+jypUTNF3nz1L5eiRUwCk\np6dz8cIVlC7OJtFfp0FNoqOiibmrIiM9g/07D9Cuq2HkmDo6lhtXbxl9rzLSM0hPSwfA3EKBVAiR\nOa+CWY2aZMTEkKlWQ0YGz4MOYdHSUHOpnj4827Ud7ePHAGhTUvT30sPOoX361KQaGzf24vatO0Rl\nzXVbtuymV68uBmV69ezCurVbAdi+3Z927VoCEB5+mVh1PABXrkRgYWGBubnhLp0qVTxwdLTn6NFT\nJtFv18CTJ5FxPL0bjzZdQ/SO4yi7NjIqV2vy20Qs9UPzPF1/TfMsTe9Ul5VSgNaoWqHRuHF9bt/O\naeetW/zo2cswArZnr06sX6dr5x3b9+rb+dmzVDQaDQClLCzIPew9eaLrHwqFGWYKM6MxsTBo0sSL\nW7eiiIy8S3p6Ops27cTHx7CP+Ph04Z9/NgOwddse2rfX9fOnT59x7NhpUrPGu6KidoOa3IuKIeau\nmoz0DAJ3HsQ7n/Hi5tXbaDON26xG3WrYOdpyMuR0UUn+Txp71cXG2qq4Zejx9KpKXJSa+Czb4ng+\ntsWVXLbFjVy2hSY9g4w0ncNaYa5Akkkm09mgUV2ics19O7fuNZr7uvXowKb1OwDw2xlAm6y5z7tD\nK65eisg19z3Qzy/vDO7H4kV/AKDVaklKSsEUKL2qkBIVx4N798lM13B19wk8OxuOcWm5nB6KMhaY\ndDD7D6p6VUUdpSbubhwZ6RmE7j5M0y6G645Lxy+SljUmRIRdxz6XzXnx6AWTOFBfhqdXVWKz+nJG\negZHdx+hSWdDzZePX8zVl69jn2W3qSNVxEapAUiOT+JBwgOs7axNqrduw1rci4wm+o6K9PQM/HcE\n0r6bYZS36p6aiCs30eazzjh55AxPHpvWtshLNa9qqKJUetv+8O7DtMhj28dHxxN1LcrIhqtQtQIX\nT1wkU5PJ82fPuX3lNo3bmWYHWv2GdbgTGa1fV/tt30+n7u0MynTq3o5tG/wA2LvrIC3a6NauntUr\ncyzLJk5MSObhg0fU9apF6rNUToSeASA9PYPLF67i7OJUKHq9GtUlKjJ7fMtg57a9dOluOL516dGB\nzRt0tv2enQG0zscP0Kd/D3ZuzXFSZzvZzczMUCgUb2xbNG3SwMCO2LhpJz4+hjuZDOyIrXvokGVH\n+Ph0ZeOmnaSlpREVdY9bt6Jo2qSBQd0OHVpz+/Yd7t41DLIwBSXNJqrg5UnCnViSsvwtYbuPUaeL\n4ffn5vErej/KnVz+Fueqrtw6eZVMTSZpz56junqXGt71i0z7/+9k/h/4KS7+TzjaJUnykSTppCRJ\nYZIkHZAkySnPfS9gHtBDkqTzkiSVzvMIayA5V/kdkiSdlSTpsiRJH2Vd+wEonVV/XVZRuSRJf2SV\nC8jnuW+EUumEKjpW/1mlikWZZ1JUKp2IidYZVxqNhocPH2FnZwtAhYpuHDqyg13+a2newnTb0Z2V\nTqhj4vSf1ao4nJXljcqoVHF6nY8ePsbWrpxBGZ++XfUTbNTtu1SpWgk3dxfkcjlde3QwifOsnJMd\nyaqciNJkdSI2TsaO9GxaDuzA5WDj6LLGPq04s+tooevLD6WyPKqYnH6hVsWhVBr3C1VMTr949PAR\ndnblqOzpgRZYv/UPAkK28sk44+19psDW2Z4kdU47J6kTsc3nhUU23oM6ciH4HACSJPHetBGsn7Pa\n5DqzcXC2577qvv7zfXUC9s72L6mRw9VzVzl/PJwNZ/5lw9l/ORNylns37/13xULE3tme+6oE/ecE\ndQL2Ti/W32VQF84GnTG5LlP3XWsbK7p0a8+RkOMm0e/o7EhsTLz+c7z6PuWdXz3ljpNLeTYeXIX/\n2W2sXrLO9NHsgNzBgcz7OZoz799H5mD4UsXMzQ25mzvlflmC7a9LMW/SNO9jTIqLixPRMTmRkTEx\naqO5LneZ7LnO3t7WoEzfvt25EH6ZtDTDF55vD+zN1i1+JlIPpZS2PMs1jzxTJ1FaaTi+2dSpSGkX\ne2IDw4zq2zaoQqeQeXQK+pHzk/40STQ7gNLFmegsewFAFaPGJe/3z8VJX0ZvU2S1c+PG9Tl5eh/H\nT+3l83HT9I53mUxG6HE/bkWdJujQUc6cKfwXoq4uSqLv5WiPiYnFxVWZp4yzgfYHDx8a9ZGixNHZ\ngThVzncvTn0fR+WrjReSJPH5zE9Y/P0yU8n7P4Gtsx2J6py5LkmdiN1L5ur2gzoRnmVbANgp7flh\n3yJ+PfEHu3/fbpJodtDZwDEGc19s/nZyVpkce74cVTwrokXL+q0rCAjZwthxuvSM1ja6Fx6Tp35K\nQMgWVqxahIPjq9kpBcXS2ZZH6iT950fqJKycjb9bDYZ14sPDC/D++h0Ozlyjv27j7shw/1m8u3Eq\nbk2qm0Rjbuyc7UnIZQMlqhNfagN1GtSZc0FnTa7rZdg525Ogzq054aV9ucOgzoQFG2v2rF8VM3Mz\n4u7E5lOr8HByLo9albP2i1PF41QAe6g4sM/TL/7LNs7N7Su3ady+MRalLLC2taZey3omC1BxUjqi\nVuX8/8Wq4nEyGi8cUecaL7LX1dcuR9CpmzdyuRy3Ci7UqV8TpavhPG9lbUmHrm05drhwgg+UyvJ6\nLTq9cSiN9JY30PswPz9Av27s3OZvcG3tluWcjwjhyeMn7NkZ8EY6XVydiY42tDVd8/gWXFyduRed\nY2s+eKCzI3T2hWFdF1fDuoMG9mHjxh0G18aOeZ9zZwP5Y8UCypUrvB1HJc0msnGyIyWXnZyiTnqp\nv6XZwPZczfK3xFy9S812XihKmVPW1grPFrUopzTNXCcQFCYlydGe7eQ+L0nSeeC7XPdCgeZarbYB\nsAGYlLuiVqs9D8wANmq1Wi+tVpsdphAkSdIlIASYlqvKB1qtthHQGBgnSZK9Vqv9CniWVT97D1xV\n4DetVlsbSAGMEupJkvSRJElnJEk6k5r2oEB/sCQZR9fkfZubbxm0xMXG41W7HR3a9GX6lLks/3MB\nllZlC/T7X12o8SVjnS8v49WoLs+epRKRtYX94YNHTP1yFkv+/Ikte1YRfTeGDI0JUpzkLyzfok37\ntqFivcocWLHL4Lq1YzlcqlcokrQx8OL/8/8sowUzuZxmzRvyyYcT6dNtMN17daJ12+ZGZQub/OLE\nXhSY0LJfWyrV9WTPcp2x0nFYN8KDzhk46k3OK3z3XoSLh5IKnhV4r+kQ3m0yGK+WXtRtZrIMU/nz\ngv///GjXrz2e9aqydflWE4sybd+Vy+X8vnI+K5ev5e6daKNnFAavMia/jDhVPIM6jqBPi0H0GtgN\nO4eiMHjzG+PyfJbLMXN1I2XCZzyY/R1WX0xEKmtpXM9EvFK7/keZmjWr8v2sr/j0U+ONZwMG+LBp\n8y6j64VFfvoNvnCSRL3vhnLx27X51k8Ou8UB70kEdZtGtXF9kFkoTKQzP5l5vn/5T+gAnDkTTrMm\n3WjXti9ffDlGnyc6MzOT1i16UbNaSxo1qkfNWtWKR/sbfj8LmzfRM2BEP44eOmHgqBcYk19/fVEb\nt+rnTaW6VfBbnuMISVIn8lW38YxvO4a2/dtj7WCi1Cv5zn2vUEarRS43o2nzhnzy4ST6dBuin/vM\n5HJc3ZScPhlGF+8BnD19npmzJppGf77tbFwubM0B/mj7BSE/bKDFp30BeBKfwu8tPmd1j2kc+n4d\nvRaPxdyyUOOSjPUW4Lvn3a8dVep5smP5NpNq+i8K0pfb9GtHlbqe7MyjuVx5Wz5dNJ7fvlxs+rEv\nX9Oi+MbbV+FNxuSwI2GcPnSa+dvnM3nJZK6dvUamiV6K/6dNoSuUTxEtm9ftJFYdz44Da5k2+0vO\nnQrXvxQHna38y4q5rP5jA/fuFFLk9ev6K3KVadCoLqnPnnH96k2DMkMGjKZRzfaYW5jnuxu+YDJf\nV+d/11UoFPTq1YUtW3OCOpYvX0P1Gi1p1LgL6th4firEc4JKmk30KnqzadS3Ne71KhO0YjcAEUcu\ncDUojHHbvmPI4k+JOnfDZN89gaAwKUmO9mwnt1dWupfco5UbsF+SpIvARKD2Kz6zfVaO9brAEkmS\nsj0L4yRJCgdOAO7oHOr5EZnlxAc4C3jkLaDValdotdrGWq22cSnzghnwKlUsLm45b0tdXJz1W+Rz\nl3F1073BlMvlWFtbkZyUQlpaOslZW0jDz18mKvIunp6VCvT7X5VYVZzB23KlixNxsfcNyqhVcbhk\nRSjK5XKsrC1JSc558eDTr5tR3rWD+0Po22Uw/boN5dbNKKJu3aWwSYlNxNYl562ordKeB/lENFVv\nVZduvv1YNmqefqtxNo16tSB8/ykyMzRG9UyBShVn8BZd6eKUb7/IfrOta28rkpNTUKniOH70NElJ\nKTx7lsrBwMPUq1/L5JqTYhMNUsHYKe1JiUsyKle7VT16+w5g0ai5+nau2rA6nYZ3Z2Ho77w7dTit\n32rHwMnGh1AWJgnqBBxdciJzHJUOJOWjNz9adW3FtbBrpD5NJfVpKqeDTlOjQdHlBQZdJJSjS06k\njYPSgaR44xcV9Vt7Mch3EN+P/M6oX5sCU/bd+b98y+3bd/hj2RpMRbw6HmfXnCid8kpH7sclvKRG\n/iTEJXL7eiQNmpl+66Mm4T4yxxzNMkdHMhMNNWfev8/zY6Gg0ZAZG4vm3j3kbm55H2UyYmJicXPN\nOfjP1VVp3C9ylcme67LTJLi4OrN+w3I+HDWByEjDeaJu3ZqYmck5H3bJZPqfqZIonWseKa2041ls\nzjxiZlkK6+rutNk2na6nf8GuoSctVn+pPxA1m0c3VGiepmJdwzRtr4qJxc0tJ+LJxVWJOtb4++eW\nx6bIm44i4votnjx5Sq1ahpGpDx48IvTISTp1LvxD4qJj1Li552h3dXU2iPjTl8ml3cba2mSpNF6F\nePV9nFxyvntOSkcSYl9tvKjXqDYD33+LnSc38tmMsfQY0BXfKaNNJbXEkhSbqE+fATrbIjmfubpO\nq3r09R3Agly2RW5S4pOJjrhLjaamsYfUqlhcDeY+Z+LyjHFqVax+ftTb88kPUKtiDea+Q4GHqVu/\nFklJKTx98hT/3QcA2L1jP3XrmUb/o9gkrHLt0rFS2vE47sXR/1d3naBqF11qGU1aBqkpurRkcZei\nSLkTj10l06R3yyZRnYBDLhvIXmlPUrxxv6jXuj4DfAcyd+SsIrGBXkZibIJBhLS90iHfvly3VX36\n+77ND6MMNZe2LM2Uv2ewYf46boRdN7neOHW8wc4zJ5fyxL/i+FZcJOTpFzrb+NVse4CNSzbyafdP\nmTp4KpIkERNpmhQhsap4g13czi7ljdbVsap4lLnGi+x1tUajYfa0Bfi0f5ePh07A2sbKYP08e+E0\nom7fZdXyfwtNr1oVp9ei0+tEbD5+AKXB+GboB+j9Vnd2bM0///rz52kE7A2ia560tAUlJlqNm5uh\nralSxxmVcXfLsTVtbKxJSkrOsi8M6+be0dGtW3vCwi4SH5/zHYiPTyAzMxOtVsuff66jcROvN9Kf\nm5JmE6XEJlEul51cTmnHw3z8LVVb1aGTbz/+HPUTmlzj24HfdrCgx1csHzoHSZJIiFQb1RUI/tco\nSY72l/ErsESr1dYFRgOlClJZq9XeAuKAWpIktQM6AS20Wm19IOwlz8ud3EpDIR8uG3b2IpUre1Ch\nohsKhYJ+/Xuyz/+gQZl9/od4513dQTK9+3bTp0ywt7fVn85d0cOdylU8iIoyTfqK8LDLVKpcEfcK\nrigUZvj060bg3mCDMgf2BdP/Hd0J0z16d9bnjwPdG9eefboYOdrtHXRGvbWNFUM/GMSGtYUfbXIn\n/BblPZTYuzkiV8hp5NOSC4GGKTTcanvw3pwPWTZqHo8THxo9o3HvVpzZXTRpYwDOn7tI5SoVqVDR\nFYVCQd/+PQjYG2RQJmBvEAPf1R2E26tPV44ePgFA8MFQatauTunSpZDL5bRo1cTgMC5TcTv8Js6V\nlDi6l0euMKO5T2vOBRrmna1YuxLvz/2YRSPn8jAxx/ha9tnPjG85mgmtP2b97NWEbgtm04/5R4YW\nFtfDr+Pq4YKzuxNmCjO8e3tzPPDEK9WNV8VTt1ldZHIZcjM59ZrXLfLUMRHhEbhUcsUpS39bn7ac\nDDQ8SLhy7cr4zvXl+5Hf8SCxYLttXhdT9d3JUz/DytqK6V/NNan+y+ev4V7JHRd3JWYKM7r26UTI\n/lf77pdXOmJRShcBbGVjRf0m9bhjgpeHecm4dg0zVzdkzs5gZoZF+w48P2ao+fnRUBReulyTkrUN\ncjd3NGpVfo8zCWfPhlPF04OKWXPdgAE+7NkTaFBmj38gg4foNo3169eDkBDdAV42NtZs2/o3M2fM\n48QJ4230b7/dm82bd5tUf/L5W1hWdqZMBUckhRy3vi1QB+RoyXj0jD21R7O/yWfsb/IZSeducnz4\nfFLCI3V15Lq5urSbA5ZVXHh6zzTOirNnL1C5Sk479x/QC/89BwzK+O85yLuDde3ct193QrJsiooV\n3ZDL5QC4u7tQtVpl7tyNxt7BDpusFBalSlnQrn0rgwPQCoszZ8Lx9KyEh4c7CoWCgQP74Odn2Ef8\n/AIZOvRtAPq/1ZPg4KKbl/PjyvlrVKjkph8vOvfpyOGAV9M03fd7fJq8TZ9mg/jlu6X4b9nPkjmF\nf6BaSedW+A0D26KFT2vO5mNbjJw7hgUj5xjYFnbO9iiydmWUtS5LtcY1Ud8yjePs/LlLVKpSEfes\nua9P/+7szzP37d8bxMB3dVHgvfp0IfSwbs4OPniUWrnmvuatmhBxXRf1GbAvmJZtdKm+Wns3N5k9\npw6/jW0lZ2zcHZEp5NT0ac7NwHMGZWw9cpyuVTp4kRylc/qUtrPS57+3cXfEtpITKXdNu1PjRvgN\nlJVcKJ9lA7X2acvpQMM0GZVqV2bM3E+YM/L7IrOBXsbNPJpb+bThdB67rVLtyoyeO5YfRs4y6Mtm\nCjMmrZhCyNYgjvsXzbh3KewqFSq741pBiUJhRo++nQnaf7hIfvfrorONXQxs4xOvaNvLZDKsyunm\nOo8aHnjU9ODc4XP/Uev1uBB2GY/K7rhVcEGhMKNXv64c3BdiUObgvhDeeuf/sXffUVFcfx/H37ML\niL2LYDdqEjVq1MQSY+9YY4wmthhN0Ri7xt5ib0mMvSv2XhCQ3hTFBhZEsGCh2QC7wu48fywizQhh\nAXl+39c5HGXnzuyH4TJz587dOx0AaNepBT5ehuOeeW5zcucxdFt80aQecTpdwkNUR44fTP4C+fhj\nonEfZu1/9iIVKpZN6Afo/FU7nBySHt+c7N3o3tPQtrfu3JpjXm/qtqIodEjWD5Anb25KWBhuimi1\nWpq3aszV4BtkxKnTfknaET2+6YytbdLpaGxtHd+0I7pZ4xbfjrC1daTHN50xMzOjfPkyVKpUAd9T\nb6YC7NGjS4ppY0qWfHOjvUvndly6ZLwbYDmtTXTb/xrFy5ekSHx/y6cdG3LRKWmbvVS18nSf/SPr\nBi5I0t+iaBTyFDKMhbX8qCyWH5Xlitf5LM3/v0xFyfFf2cWoHcPZqCDwunXcL70rK4pSAqgA3ATq\nA1Gqqj5TFOWj+O9fi1UUxVRV1djUtmNsOp2OcWNmsHv/OjRaLdts9nAl8CrjJg7F7+xFHOxd2bp5\nN8tXL8DXz4noqBh+7D8CgAZffMa4icOIi9Oh1+kYPXxKkjvHxs455ffZbN69Aq1Wy65tBwi+co2R\n4wZz3i8AZwd3dm7Zz58rZuNxypbo6BiGDHwzu0+9hnUID4tM8RG2qbN/p2p1w0fQ/16wihvXbho9\nu16nZ+eU9QzZPBGNVoPPLjfCg+/QYcQ33LxwjQvOZ/hqfG9y5TFn4PKRAESF3mflj/MBKFK6OIUt\nixF8IsDo2d5Gp9MxYcxMtu9di1arYfuWfVwJvMrYCb/hd+4ijvZubLPZw9JV8/A560B0VAw//zAK\ngJiYR6xathEH192oqoqLkyfOjh7veMeM0+v0bJ6yljGbp6DRavDc5UJo8G2+GtmTG+evcc75FD0n\n9MU8jzm/LR8NwIOw+/w5MHM7Tv8t79LJy5m9ZRYarYajOx25GXSTvqP6EHQ+mBNOJ6hSswpT10wm\nf8H81G9Zjz4j+/BTy5/xOuJNrYa1WO20ElVVOe1xhhPOJ9/9pkbOv3LyCmbY/IFGq8FppxO3gm7R\na2Rvgi8E4+t0kh8mDsA8jznjVowH4F7YPf4YMOMdW86YzKi7llYWjBjzC0FXruHkaZj+Zv3qbWyz\n2ZMp+edNWMyy7YvRaDUc2nGE60E3+GXMAAL8A/F0PEbVmh+xaP1sChTKT+NWX/DLmAF0b9qHCpXL\nMXLqkPiPoILNyu1cDTR+h2QKeh2P//mLQvMWomg0PLe3Q3czhLzf/0DslUBe+Rzn1SlfzOp+RpH1\nm0Cn58nqFaiPDI3cQn/9g0mZsii5c1N0x24eL5zPq9PGfTijTqdj1MgpHDy0Ga1Wy+bNu7h8OZhJ\nk0dw9uwF7I44s2njLtauW8z5C+5ERUXTr+9vAPz8S18qflCOceOHMm78UAA6dezDvXuGT3B81c2a\nr7r2N2re5FSdHr8JG/li+zgUrYab2915fCWUj8d+TbTfdcId334xXvTzD/nwt07oY+NAr+I3bgOv\nHj7OlJw6nY4xo6ax/+AmtFoNNpt3E3g5mImThnP27AXs7VzYvGknq9cuxu+8K1FRMfTvZ9inDRrW\nZcTIX4iNi0Ov1zNy+BQePoiiWvWPWLl6AVqtFo1GYf9eOxwcXDMl+/DhkzliuxWNVsOmjTsJuBzE\n1CmjOXPWH1tbJzZs2MHGDX8TEOBN1MNoevcZnLB+0BUfChTIj5mZKZ06tsHa+jsuBwYbPWfyzPMn\n/sWSbQvRajUc2mHH9aAQfh7zA5f9ryQcL+avm0mBQvlp1KohP4/+gR7N0t2MzTJjps7l1LnzREc/\nokWX3gwe0IduyR4ml5X0Oj0bp6xh3OapaLQa3OPbFl+P/Jbr569y1vkUvYXEqt8AACAASURBVCb0\nwzyPOUOXG6ZVeRB2j0UD52BVqTS9J32PqqooisKR1Qe4fSVzbn4azn2z2L53DVqthh1b9hMUeJUx\nE4bgf+4SjvZubLfZyz+r5nH8rAPRUdH88oOhLWQ4923C3nVXwrnPxdHQoTlr2mL+WTWXGXPG8eB+\nFCN+nZgp+VWdHucpm+i+eSyKVsOFXR48CA6l0chuRJy/wVXns3zarzXlG1VDF6vj5aOnHBlpuDFU\npt5HNBrZDX2cDlWv4jhhAy9inmZKztf0Oj1rJq9kqs10NFoNLjuduR10i29H9uLqhWBOOfnSb2J/\nzPOYM2bFOMDQBpozYCYAs/bMpdQHpTHPa86akxtYNmYJfp4pn69h7Mxrp6xi0uZpaLQaXHc5cyf4\nNj1Gfse181c57exLnwnfY54nN6OW/w7A/bB7zBs4iwYdGvHx59XIVyg/Tb82PIRy2ei/CQnIWMfk\nv9HpdMwav5DVO5ag0WrYv/0w167cYMjYn7jkfxm3o15Ur/Uxf2+YT4FC+Wna+kt+HfMjnZt8C8Dm\ng6uoUKkcefLmxuXcYaaMmMkx98xtK+t1elZMXsFMm5lotBocdzpyK+gWvePbxiedTlK5RmUmr5lM\nvoL5qNeyHr1H9mZQy0FoTbUs2LsAgGePn7Fw2MJMm75Cp9Mxfdw8Nu5ehkajYc+2QwRfuc7wcb9w\nwS8AFwdPdm09wKLlf+Dqe5Do6BiG/WhoyxctVpiNu5eh16tEht9l1KDJgGGO9F9HDeRq0A0OuRpG\ns9us28muLQfemiM9eSePnc3WPavQaLXs3LqfoMBrjB7/K/7nLuHk4M6OLfv4e+UcvE/bER0Vw+CB\nb6a5qt+wLuFhkUmmfcyTJw/rty4lVy4zNFoNxz1PYrNhV4ZzDhs+iSNHtqHVaNi4aScBAUFMnTqa\nM2cM7Yj1G3awceMSLgd4ExUVTa/ehnZEQEAQu/cc5ry/G3E6HUOHTUx4YG7u3Oa0bNGYwYN/T/J+\nc+dMombNqqiqSsjNOymWZ/RnyUltIr1Oz74pG/hp8wQ0Wg2+u9yIDL5D2xHduX3hOpecz9BxfC9y\n5clFv+XDAUN/y/ofF6I1NWHI7mkAvHzynK0jlsrUMSJHULJz/sr0UBTliaqq+RJ9/z1QV1XVIYqi\ndAb+xNDZfgL4TFXVpsnKJPw/fv0Q4DGGkeimwCJVVdcripILOACUAq4AxYFpqqq6K4oyD+gEnAUm\nArbxU8+gKMpoIJ+qqtPe9jMUK1AlZ+zseHlN0/XBgPeCdf6snaLDGPZFX8juCOnSquDH2R0h3SL1\nz7I7QrqZKdrsjpAupx9lQaexkVnlzlkP03GomrPqBEAFH+PfIM1sNgUaZneEdPn+cdpG5L1PXsS9\neneh90zNohWzO0K6HD+/Mbsj/Cf96ozK7gjp4haT+dN0GNOIgrWzO0K6+ajZP+I8PbSpTUj8nrv8\nIvLdhd4z5XLlrDbclWeZ+4DazPBSn/PO1ZFPMueh1pkl1Tn533NDLBtld4R0WxyyI+ft6PfAEYtv\nc1T/ZWqsI7dny+8+x4xoT9zJHv/9RmBj/P8PAgdTWSdxmYT/x39f/i3v8xJo95ZlvwOJb0dWT7TM\nuJ/DEkIIIYQQQgghhBBCiCykl9sT/9n/lznahRBCCCGEEEIIIYQQQohsIR3tQgghhBBCCCGEEEII\nIUQGSEe7EEIIIYQQQgghhBBCCJEBOWaOdiGEEEIIIYQQQgghhBCZR49M0v5fyYh2IYQQQgghhBBC\nCCGEECIDpKNdCCGEEEIIIYQQQgghhMgA6WgXQgghhBBCCCGEEEIIITJA5mgXQgghhBBCCCGEEEII\ngZrdAXIwGdEuhBBCCCGEEEIIIYQQQmSAdLQLIYQQQgghhBBCCCGEEBkgHe1CCCGEEEIIIYQQQggh\nRAbIHO1ZqGORT7I7QrpE6p9nd4R0c312I7sjpNvD54+zO0K6uCpXsjtCuj2LfZndEdLNIk/h7I6Q\nLnF6XXZHSDdVzVkzz90JKpTdEdKtbL6cdx6pWiA6uyOkyyfa8tkdId0uP7qd3RHSrbdJ2eyO8D9h\n05lF2R0h3brW/i27I6TZL23vZXeEdFu770F2R0iXODXntYcevXqa3RHSLTaHtTvvPc9ZbQsAE402\nuyOkW6Hc+bI7Qrq80sVld4R0OxeXs47J4r/TZ3eAHEw62oUQQgghhPh/pF+dUdkdId1yYie7EEII\nIYQQicnUMUIIIYQQQgghhBBCCCFEBkhHuxBCCCGEEEIIIYQQQgiRATJ1jBBCCCGEEEIIIYQQQgj0\nipLdEXIsGdEuhBBCCCGEEEIIIYQQQmSAdLQLIYQQQgghhBBCCCGEEBkgHe1CCCGEEEIIIYQQQggh\nRAbIHO1CCCGEEEIIIYQQQgghULM7QA4mI9qFEEIIIYQQQgghhBBCiAyQjnYhhBBCCCGEEEIIIYQQ\nIgNk6hghhBBCCCGEEEIIIYQQ6LM7QA4mI9qFEEIIIYQQQgghhBBCiAyQjnYhhBBCCCGEEEIIIYQQ\nIgOko/09V71JLWa7LGGu+1LaD+qaYnnrAR2Z6fQXM+wXM2brVIqWKp6wbN21XUy3W8h0u4UMXTMu\nyzLXaVKH1W6rWeu5lu6Du6dYXv3z6iw5soTD1w/zRfsvkizrP74/y52Ws9xpOY07Ns6qyAkaNWuA\n/fE9HD25jx9/65died36n7LX2YaLYT606dA8S7O1bt2Uixc8CAjwZszoX1MsNzMzY+uW5QQEeOPt\ndZhy5UoDUKRIIRyP7uLhgyv89dfMhPK5c5tz4MAmLpx3x++cC7Nmjjdq3qYtGuHpa4v3GXt+HT4w\nlbymrFi3EO8z9hx22k7pMlYJyz6uVoVDR7fievwgzsf2kyuXGQCdurbFyXsfrscPMnH6KKPmTa5F\ny8b4nnXkjL8Lw0f+nEp+M9Zt+psz/i44ue2hTNlSSZaXLm3J7Qh/hgwdkKk53+Z9rcvNW37JiTMO\n+Po5MXTETymWm5mZsnbDX/j6OXHUdXeK/VqqtCUhYef49bcfEl77aVBfvE7Y4n3yCD8PTvmzGlPD\nZvXY772dgz476T+kd4rltevXZJvjek7d8aBlh6YJr1uWtmDr0XXscN7IHo8tfN23S6bmTKxA00+p\n6r6cql4rsRjcLcXyIt2b84nfZj5y+JOPHP6kaM9WCcs+sJlKjYtb+WDDpCzL26hZfeyO78bh5F4G\n/tY3xXJD3d3MhbDjtE5Wd1fv+JuTwS6s2LI4q+ICkPfLOlRwWE1Fp7UU+Snlea9g15ZUOrGd8gf/\nofzBfyjYvU2S5Zq8ufnAazMWUwZlVWTqNf2M7Z6b2OltQ+9fv02xvGa9Gqx3WIXHTSeaWic9H3ve\ncmKj42o2Oq5m3oaZKdY1lhYtv+Tk2aOc9nNm2MjUjhdmrNv4F6f9nHFyTXkcLlXaklvhfgnH4Vy5\nzHBy24Pn8UMc97Vj3IShmZYdoFyTGvR1W0A/z0XUHdwxxfJPejenl+McvrOfRfe9kylS+c15sNhH\nZfhm/1R6O8+ll+MctLlMMzXrazWafMpC16Us9lhOx0FfpVjefmAn5jsvYa7Dn0zYNp1i8e3OYqWK\nM8t2IbPtFjPf6W9a9GqTYt3sMGn2Yhpb96RL71+yO0qqajepw0q3Vaz2XMPXqbSZuwzswnKXFfxz\ndCmzts+ieKJ2flbSVqtL3ulryffHBszafJNqGZM6jck7dTV5p64m94A31x25vhpgeH3aGnL1yLpj\n3GtfNm+Ag89enHz389PQVNpDDT5lv8sWAsJP0KZjiyzN1rh5Q5xP7MfV9yC/DO2fYrmZmSlL1s7F\n1fcg+45uplQZSwBMTExYsHQG9p67cDy+l0HDDG2iCpXKYeu2I+HL/4YX/X/+zmh5jd2Gq1SpAm7e\nBxO+btw5a/R23Lt+/6Zmpvy1ZjZOvvvZ7bAxYR+bmpowZ8kUDnvs4JDbNj5vWCdhnfZdWnHIfTtH\nvHYyZorxzyMtWzXmzDln/M67MmJUymOXmZkZGzYtwe+8K67u+ygbv5/r1KmBt48t3j62HDtxhA4d\nWyescyHAEx9fe7x9bHH3OmjUvMY+VwP4XXTD+4QtHscO4eKxz6h5AZq3+BKf0w74nnNk6IgfU8ls\nypoNf+J7zhEHl10JmcuULcWtCH/cvA7g5nWABX9OBwzX1dt2reL4KXu8TtgyeZrxr1NbtGzM6bNO\nnPN3ZcRbrk03bFrCOX9XXNz2JtSL2nVq4HX8MF7HD+PtY5ukXgz+tT8nTtnj42vPug1/JVxzG9tn\nTeuyyWM9W7w38u2vPVIsr1HvE1bZL8c5xIHG1l8mvF6rYU3WHF2Z8HX06hG+aNMwUzIKYUxpmqNd\nURQdcAFQAB0wRFXV42l9E0VRpgFPVFVd+F9C/leKonwKnAXaqqp6NP618oCtqqrV07GdfMACoDXw\nCMN0RStVVV1j7MxJ3lejoc+MH1nYewYPIx4w5dA8/JxOEXb1TkKZWwE3mNFxLK9evKJZ7zZ8M74P\nK4YYOhhevXjF1PajMzNiChqNhsEzBzOx10Tuh9/nr8N/ccLpBLeDbyeUuRt2l8WjFtPt56QdPp81\n/4xK1SsxpO0QTM1Mmb97PqfcTvH8yfMsyz5l3lh+6D6EyLBIdjtuwvWoJ9eCbiSUCQ+NYPzQ6fww\nOGUHW2Zn+/vvmbRv/x137oTjc/wItraOXA4MTijTv39PoqJjqFq1Ed9078TsWRPo1XswL168ZNr0\nBVSr9iHVqn2UZLt//rkKD4/jmJqactRhB23aNOPoUTej5J21YCLfdv2R8LBI7Fx34mjvRvCVawll\nvu3TjZiYRzSq045OX7Vj4rSRDBowGq1Wy5JVcxn2y3gCLl6hcOGCxMbGUbhwQSbNGE3bpt15+CCK\nv5bPplHjenh7nsxw3tTyL1g8ja6d+hEWGoGr5z7s7Vy4Eng1oUyfft2JiY6hTs0WfPW1NdP+GMuA\nfsMSls+aNxFnJ0+jZ0uL97UuazQa5i2ayted+xMWGoGT+14c7FwISlQvevXtTnR0DJ/XakXXbtZM\nnT6Ggf2HJyyfOWcCLon260cfV6ZPv29o3exrXr2KZde+dTgddef6tZuZkn/cnFEM+mY4keF32eqw\nFg9Hb64HhSSUCQ+NZOqwWfQdnLTj8l7kA77v+Auxr2LJnSc3ezxs8Djqzb3I+0bPmSw0ZWb+TPB3\nU4kNf8CHtguJcfLlRaJjMkDUYW/uTF6dYvW7K/ejyZ2LYlnUaabRaJg8bywDug8hMuwuuxw34XbU\nK0ndDQuNYPzQGanW3fXLtmCeOxc9+qbsIMzE0FhMHczt/hOJjbhP+b1/8cTlBK+uJd3Hj+08iZyx\nItVNFBvel2e+F7MiLWDYz6NmDWP4t2O4G36PtXYr8HY8Tkjwm7+byNBIZo2Yx7e/pOxUe/niFd+3\nTnkxbeyM8xdN46vO3xMWGoGLx14cjrhy5cqb43Dvvl8THf2IurVa8lU3a6bNGMOA798cL2bPnZjk\nePHy5Su6dOjL06fPMDExwd5xB85Onpw+5Wf0/IpGoenMfuzvNZcn4Q/peXgG153O8DA4LKHMlQM+\nXNjiCkCFVrX5cnJvDvadj6LV0ObvQRwdvpL7l29hXigf+tg4o2dMmVlD/z9+Yk6vaTyIeMDMQ/M5\n6+xLaPCbdmfIpetM6jCaVy9e0bJ3G74d35d/hiwi6m4UU78aR9yrOHLlMWe+49+ccfIl+m5Upuf+\nN13at+K7bp2Y8EeWXoKkiUajYdDMQUzqNYkH4ff58/CfnEzWZr526TojrIfz8sVL2vVuT/8JPzD/\n13lZG1TRkPvbX3n613jUqPvkHf8PcedPoA+/9eZnKWFFrrY9eLpgJDx7gpK/IADailXRflCNpzMM\nnYV5xi5CW6UGuqDzWRJdo9Ewde7v9O/+KxFhkex13IyLQ7L20J0Ixv02jQGD+2RJpsTZps8bR9+v\nBxERFskBp604O3hwNeh6QplvenXhUfRjmn/emQ5d2/D71GEMHTiO9p1bYpbLjHaNv8E8tzmOx/Zy\naJ89N67epEOzngnb97lwlKNHMt6uf709Y7fhrl69QbNGnRO2f+GKF0cOOxkl7+ttvuv3371XZ2Ki\nH9Pq865Yd2nNmCm/MfzHCXzTxzDArWOTnhQpVpi1O5bQrVVfChYqwNipw+jasjdRD6KZt3QaDb78\nDB+vU0bLvGjxdDp37EtoaATuXgewO+Kc5Bqkb79viI5+RK0azen2dQem//E7/fsNJSAgiCaNOqPT\n6bAoWZzjJ45gb+eCTqcDwLrddzx8YNxjcmacq1/rZN3H6HlfZ567aArdu/QnLDQSR7c9ONi5plKX\nH/H5p63p0q09U6aP5sf+IwAIuXGLZl+mHDCz7J/1HPM6iampKfsObaRFy8a4OBvnWtBQL6bRpVM/\nQkMjcPPcj12ya9O+/Qx/f5/WTFovLgcE0fTLLoZ6YVGcY/H1okSJYvwyqB+f123Dixcv2bh5Cd2+\n7si2rXuNkjlx9mEzf2PMd79zL/w+K48s5bijDzeD35xDIkPvMm/kAnr8nPSGs99xf35sYzh/5C+U\nny3eGzntccao+cTb6ZXsTpBzpXVE+3NVVWupqloTGA/MMcabK4qS2Q9j/Rbwjv83I9YCUUBlVVU/\nBdoCRZIXUhRFm8H3SaJirUrcvRnBvduR6GLj8D3szaetP0tSJtDnIq9evALg2rkgCpcsaswI6Val\nVhXCQsKIuBVBXGwcnoc9adC6QZIyd+/cJSQwBL0+6eMVylYuy4UTF9Dr9Lx8/pLrAdep27RulmWv\nUbsat27c5s7NUGJj47Db70SLtk2SlAm9HU5QwFVUvZpluQA++6wW166FcOPGLWJjY9m16yAdE92N\nBujYsTU2NrsB2LvvCM2aNQLg2bPnHD9+ihcvXiYp//z5Czw8DPfLYmNjOed3kVKlLI2S99M6nxBy\n/Ta3bt4hNjaWg/vsaNO+WZIyrds1Z/d2w4iKIwcdadSkPgBNmjfk8qUgAi5eASAqKga9Xk/Z8mW4\nfjUkocHl5eFD+05J94Gx1Klbk+vXb3Iz5DaxsbHs23OE9tYtk5RpZ92S7Vv3A3BwvwNNmr6p5+07\ntOTmjdsEXg4mO7yvdbl23RrcSLRf9+89QrsU+7UFO7Yb9uuhAw58mWi/trNuyc2Q20kalVU+/IAz\np/x5/vwFOp2O48d8se7QisxQ/dOPuX3jDqG3woiLjePoAReatvkySZnw2xEEX76GPtl+jYuNI/ZV\nLABmuUxRlKxpueStVZmXIRG8uhWJGhtH1CEvCrb+PM3rPz52Hl0W3eyE13X3DnduhsXXXUeat006\nmjosvu4mP4cAnPA6xdMnz7IqLgDmNarw6mYYsbcjIDaOR0c8ydeywbtXjJerWiVMihXimffZTEyZ\n1MeffsSdkFDCboUTFxuHy0FXvkw2QijiTiTXLl9HTWU/Z4U6yY4X+/YeoV2HpKNN21u3ZMc2w0i3\ngwccaJzsOBwSkvI4/PSpoX6YmppgYmqCqmbOMdCi1gfEhETy6NY99LE6gg6foGLrOknKvEr0t2Wa\nOxfEZynX+BPuX77N/cuGi9AX0U+y5FhdqVZlIkPCuRvf7vQ57E2dVkmPFwGJ2p3B54IoYmlod+pi\n44h7ZbgZYGpmiqJ5P67O6tb6hIIF8md3jFRVqVWF8JAwIhO1meu3rp+kzAWf87yMb79dORdIMcti\nWZ5TW+FD9HfDUO9HgC6O2NPumNRMeowzbdSOV+6H4dkTANTHMfFLVBRTMzAxARNTFK0J6qOsu/lS\no3Y1bobc5nZ8e+jIAUdatkvZHroScBW9mrXHupq1q3PzxptstvuP0qpd0yRlWrZryt4dhwGwP+RM\nwy8Nf4+qCnnymKPVajE3z0VsbCxPHj9Nsm7Dxp9zM+QOYXfCjZI3M9pwiTVu2oCQG7e4czss1eX/\nRVp+/y3aNWH/TlsAHA670CB+H1f6sAI+nobO84f3o3gc85hPalWlTLlShFy7SdSDaACOe/im+HRd\nRtSNvwYJid/Pe/fYpmjXWndoyfb4ztAD++1p2tRw/n7dFgYwz5WLTDq9JZFZ5+rMVLtODUKu3+Rm\niOE69cC+I7SzTpq5Xfvm7NxmqMuHDxzlyyb/3q57/vwFx7wMg79iY2M57x+AZSkLo2Wuk6xe7Ntj\ni3Wyv7/21i3ZttWwnw/st0+4Nk1SL8xzJWn3aE1MyJ3bcCzJnTs3EeGRRsv82ke1PiQsJIzw+HOd\n60F3vmidtM0ZeSeS65dvpLh+SqyJ9Zf4up1KOCcK8T77L1PHFMDQ6QyAoihjFEU5pSjKeUVRpid6\nfaKiKFcURXEGPkz0uruiKLMVRfEAhimKUk5RFJf49V0URSkbX+5tr29UFGWFoihuiqJcVxSliaIo\n6xVFuawoysZE76MAXwPfA60VRTFP9DOYKIqyKX7bexRFyaMoSjtFUXYlWr+poiiHFUX5APgcmKSq\nhhaYqqr3VFWdl6icm6Io2zCM+jeawhZFeBj2ZrTjw/CHFLZ4e0d6429acMH9zYW6aS4zphyax6T9\nc/g0HR0rGVG0ZFHuJ8p8P/w+Rf8lc2LXA65Tt1ldcpnnokDhAtRoWCNLLygsShYnPPTNySUiPBIL\ny+z5iG5ypawsuXP7TUM5NDQCq2Sd4qWsSnInvjGt0+mIefSIokULp2n7BQsWwNq6JW5u3kbJW9LS\ngrDQN3nDwyIpaZm0sVHSqgRhoREJeR89ekzhIoWo+EF5UFW27lmNg/tuBg01fLw05PotKlWuQOky\nVmi1Wtq0b4FVqZJGyZucpZUFoYkuTMJCI7C0SprfKlEZnU7Ho5gnFClamDx5cjNsxM/Mm/NPpmRL\ni/e1LltaWhB2JyLh+7CwlPvV0jLZfn30mCJFDPt16IgfWTB3aZLylwOCafBFXQoXKUTu3Oa0bN0E\nq9LGuWGUXAnL4kSG3U34PjL8LsXTsV8trEqw03UT9mf2s3HZ1swfzQ6YlizKq0TH5NjwB5imckO2\ncLsGfOz4NxVW/o5pNnTkvFaiZHEiEtXdyPC770Xd/TemFkWJi3izj+Mi7mOaynkvf+svKH9oGVZL\nJmBSMn4fKwoW4wZyd966rIoLQPGSxbibqC7fDb9P8ZJp389mucxYZ7eC1YeX8mWbL969wn9gaVmS\n0NBkx+Fk5xHDsTrReSTJcfgn5qdyHNZoNHgcO8SV6ydwdzvGmdP+mZI/X8nCPA57mPD9k/CH5LNI\neU6u0bcl/bwW0WhCTzymbgagUMWSqKh0sRnLt0dmUucX60zJmFzhkkV4EJ643fmAIv8ygKNZj5b4\nJ2p3FrEsylyHP/nnxBoOr9yf7aPZ33dFSxblXjrazK17tOaM2+msiJaEUqgo+qh7Cd+rUffRFEp6\nntBYlEZjUYo8YxaT5/e/0FYzDJTRXb9M3BV/8s/fTv4F24m7dAZ9RNJP+2QmC8sSSc4pEWF3sbAs\nkWXv/29KWpYgPOxNtvCwlG01C8sShCdqKz9+9ITCRQphf8iZZ89ecOKSE95+9qxZtpmY6EdJ1u3Y\ntQ2H9zkYLW9mtOES69rNmn17jhgtL6Tt929RskRCm/nNPi5I4MVgWrRrglarpXRZK6rV/JiSpSy4\neeM2FSuXp1QZS7RaLS3bNzVqh6plous5gLDQcKxSOffdSb6f46/56tatyclTDvj42jN86KSEDlZV\nVTlwaBMe3gf5vn9P4+XNpHO1qqrsPbABV8/99OufcqqRDGW2siA0NFFdDo1MkbmkpUXCz5W4LgOU\nLVcaV6/9HDxiQ/0GSW+gAxQomJ/W7Zrh5eFjtMxWya5NQ1O5NrW0Kpns2vRNvahTtyYnTtlz/KQd\nI4ZNRqfTER4eyT9L1nLxshdB13x49Ogxrq7G6QtIrJhlMe6GvzmH3Iu4/5/6eJp1aorLAeN8QkeI\nzJbWEeW5FUXxA8wBS6A5gKIorYHKGDqiFeCQoiiNgadAT+DT+Pc4CyT+jEchVVWbxG/jMLBZVdVN\niqL8ACwBugBL3/I6QOH4DJ2Aw8AXwEDglKIotVRV9Yt/7YaqqtcURXEH2gOvJ/j6EBigquoxRVHW\nA4OBv4BViqLkVVX1KdAD2AlUA/xfd7K/xedAdVVVbyRfoCjKT8BPAA2KfMqH+Sv8y2ZSrJzipbeN\nvGrQpTHla3zA3B6TE14b3fBnou9GUbyMBWO3T+NO4E3u3TL+XcrEUhulmdbRYue8zlGlZhUW7l/I\no4ePCDwTiF6XhaNLMpA9s6U2+DV5tv+677VaLTY2y1i2bD03btx6Z/m0SFNeUi2E1kTLZ/Vr0755\nD54/f8GuA+u44HcJb8+TjB/9ByvWL0LV6znt60fZ8qWNkjdl/jTsy7eUGTdxGCuWbUgYNZkt3tO6\nnJb9mmoZVH6fMJSVyzam2K/BQddY8uca9h4w7PNLFwLRxWXSFAupV+w0rx4ZdpcezftR3KIYizfO\nwfmwGw/vZ3JHVGqDSpNljnE6RdRBT9RXcRTr3Zbyfw4juOfkVFbMfKnXkWwIkh5pqBeP3U7yyNYd\nNTaOQj3bYzlvFLf7jadQL2ueeJxO0lGfFTJyrgbo9nlP7kc+wKqsJUt2LeJ64A1CbxpvFCJk7Lw3\nbuJQVixN/Tis1+tp8kUnChTMj8225Xz8cWUuZ8ZIujTW5fObnTm/2ZkPOzfgs6FdcBq5Co1Wi1Xd\nKuzoOIW456/4avt47l4I4faxS8bPmThyKgeMt9WLL7o2ocInH/BHjzfPb3gY/oBxbUdQqERhRq0Z\nz0m74zy6H5Pq+oI01xGApl2bUalGZcZ983smh0pNqieSpN9qtGhKlOLZojEohYuRd8winkz/GSVf\nQTSWZXg8rhcAeYbPQRtQHV1w1kyVlZbjSLZJQ5Pibce4mrWrodfpaFC9NQUL5Wen7XqOeZzk9s1Q\nwPCJnRZtm7BgpvEGfWRGG+41U1NT2rZvwcxpi4wTNiFPytdSZk6tkRQ1SQAAIABJREFUDOzZdoiK\nVSqwz3kzobcjOHfqPLo4Q+fl1DFz+WvNHPR6PedOnadMuVIpN5KZmf+l8pw+7U+9z9pS5cMPWLV6\nIU6O7rx8+YrWLboTEXGXYsWLcvDwZoKCrnH8WManu8msc3W7Vj0NeYsVYd+hjQQFXcfHCHn/LU9a\nykRG3OXTas2IioqmRq1qbN66jEb1rRM+UaLValm9bjFrV9pwM+ROim0YN3PyMinXe/1znTntT/3P\n2lHlww9YuWoBTo7u5M5tjrV1S2pUb0pM9CM22Szlmx6d2bXTuHP4p6dt8TZFShSh4kcVOOWR9Teb\nhfgv0trR/lxV1VoAiqI0ADYrilIdw5zlrYFz8eXyYeh4zw/sV1X1Wfw6h5Jtb2ei/zcAXk+magPM\nf8frAIdVVVUVRbkARKqqeiH+fS4B5QE/DNPF7IgvvwPow5uO9tuqqh6L//8WYKiqqgsVRXEAOiqK\nsgewBsYCSea7UBRlItAdKKGq6uunVvmm1skOoKrqamA1QP/y3dJ1RImKeEARqzd3+4pYFiH67sMU\n5ap+UYMOQ7oxt8fkhI/tAgkjie7djiTwxCXKVauQ6R3t98PvUyxR5mKWxXiYSua32bl0JzuXGqrH\n2CVjCb0RavSMbxMZfjfJiISSlhbczeLOj7e5ExpO6TJvRumWKlWS8LCIlGVKWxIaGo5Wq6VggQI8\nfBj9zm2vWD6Pq1dv8M8/xhtRGR4WmWTEvaWVBZERd1MpU5LwsEi0Wi0FCuQnKiqG8LBIThw7TVR8\ndlcnL6rXrIq350mcHNxxcnAHoFe/7ugyaUqDsNAISiUaFW1VqiQR4XdTLRMWFmHIXzAfUQ+jqftZ\nTTp3acv0P8ZSsGAB9Ho9L1++Ys0qm0zJmpr3tS6HhUVgVfrNpxCsrFLZr2GG/ZqkXjyMpnbdmnTs\n3IapM8YY9quq58XLV6xbvYWtNnvYarMHgIlTRhKW7G/DWO6G3cXC6s1IKAvLEtz7D/v1XuR9rl25\nQe36NXG2dTdiwpRiwx9gluiYbGpZlNjIpMdkXfTjhP/f3+ZIqfEpH0CaVSLD71IyUd21sCzB3Yh7\n/7JG9ouNuP9mhDpgUrIYscnOe/pE+zh6lwPFxxgeepe71sfkqVuNwt9Zo+Q1RzE1Rf/sOfcWbszU\nzHfD71EiUV0uYVmM++n4hMX9yAcAhN0K55yPH5WrVzJ6R3tYWESS6cysSpUkIiK143DJFMfhOnVr\n0qlzW6YlOg6/ePGStau3JKz7KOYxx7xO0qJV40zpaH8S/pD8Vm9mGcxnWYSn/zLC+8qhEzSb1R+n\n+HVDTwbyIsowDUeImz/Fq5fP9I72hxEPKGqZuN1ZlKjIlG246l/UoMuQr/njm0lJ2p2vRd+N4k7Q\nLT76vCq+dsYb0ff/zYPw+xRP0WZ+kKJczUa16DGkB+O++T3V/Z3Z1Oj7aAq/GWmtFC6GPjppTjXq\nProbl0GvQ30QiT7yDpoSpTD5sAa664Hw8gUAcRdPo63wcZZ1tEeEJT2nlLR6f84pEWF3k4xItbSy\nSJEtIiwSy/g2qFarJX+BfERHxdCpWzs8XI4TFxfHg/tRnDnpxye1qiZ0tDdp2YhL5wO5fy/t12Dv\nklltODA8/PO8/yXu3UtZ/zMiLb//iPg2c2SyfQwwZ/KbB6vvOLKOkOuGAUlujl64OXoB0KNPV3RG\nHBgWFhpB6STXIJaEJz/3hRnKJJz7CuRPcc0XdOUaT58+o2rVDzl37kLC+fP+vQfYHnKkTt2aRulo\nz6xzdULe+w85ctiJOnVqGK2jPSw0glKJPhVtVcoiRebw+J8r6XWqYR+/emX497zfJUJu3OKDShXw\nP2c4pi3++w+uXwth1YpNRsn6Wmiya9NSpUqmmOYl5bVp/oTr6deCrlzj6bPnVK36IeXKl+ZmyG0e\n3DccJw4fOkq9+rWN3tF+L/weJRJ9Wqd4yWI8iEjf33qzjk3wdjiGLk5n1Gzi3+lTvdEu0iLdU8eo\nquoDFAOKY7gXPyd+/vZaqqpWUlX1dW/dv3UqP/2XZW9bL/Hrrydm0if6/+vvTeLnSu8GTFEUJQT4\nB2inKMrrSRqTv8fr73cC32AYLX9KVdXHQABQU1EUDYCqqrPibzoUSOPP85/d8L9KifKWFCtdAq2p\nCZ93bMQ5p6R38cpWq0C/2T+zZOBcHj9485HBPAXyYmJmuI+Sr3B+Ktf5iLBg491VfZsg/yCsKlhh\nUcYCE1MTGndszAmnE2laV6PRkL+Q4VdU/qPylP+4PGc9s27O2gvnAihXsSylylphampC+66tcD2a\nPQ+zTO70aX8qVapA+fJlMDU15ZtvOmNrm/RhQba2TvTpY3iASLevrHF3P5bappKYPs3Q4B01aqpR\n8/qdvUiFD8pSpmwpTE1N6fxVexztk37Uy9HBje7fGh6AZN25NcfiH2rq4XKMj6tVwTx+vrj6X9RN\neIhq0WKGTouCBQvQb0BPtm/eY9Tcr509c54PPihH2XKlMTU15auvrbG3c0lSxsHOhW97GR6U1Llr\nWzw9DPW8fetvqVmtKTWrNWXF8o0sXrgiSzvZ4f2ty+fOXKBixfIJ+7VrN2scUuxXV3p+a9ivnbq0\nTfjYZce231H7k+bU/qQ5q1Zs4q+FKxMu0IrF14tSpS3p0Kk1+/bYZkr+S36BlK1YGquylpiYmtCm\nSwvcHdP2EcsSlsXJZW4GQP6C+an12SeEXDXOJ0j+zVP/YHKVt8SsTAkUUxMKd/qSGCffJGVMSryZ\nzqJg6895cTXzzxVvY6i7ZRLV3da4HfXKtjxp8eJCEGblrTAtbQGmJhSwbswTl6TnPW3xN/s4X4t6\nCQ9KDR+9gGtNv+da8/7cm7uORwdcMr2THSDQL5DSFUphWaYkJqYmtOjcHG/HtHWI5i+YD1MzUwAK\nFi7AJ59VJyTI+A8fPnvmAhU/eHO8+KqbNQ5Hkh4v7O1c6PmdYUxG5y5t8Yo/Dlu3+Y5a1ZtRq3oz\nVi7fyJ+LVrJ29RaKFitCgYKGdoa5eS6aNGtIUKIHDxpTpP91ClUoSYEyxdGYaqnSsT7XnZK2aQqV\nf9MBVKFFLaJDDDcJb3qep9hHZTExN0PRaihV/yMeBmf+wINr/sGUrGBJ8TKGdmeDjo0445S0Y6Nc\ntQoMmDOIRQNm8+jBm9HqRUoWxTSX4RiXt0BeqtT9mPBrWTdYIicytJlLJWkzn3RK+oD3itUqMmTO\nEP4YMIOYB9nz6QBdyBU0JUqhFLUArQmmdZsS55/0GBfrfxzthzUBUPIWQFOiNOr9cPQP72FSpQZo\nNKDRYlLlE/QRmX/ue+3CuQDKVyhD6fhzinWX1rg4ZH97COD8uUuUr1g2IVuHrm1wjh9M8pqLgwfd\nenYEoF2nlgkP3Ay7E0HDLw3P7cqdx5xadWtwPTgkYb2OX7U16rQxkHltOICvundg327jt93S8vt3\ndfCka48OALTt2AIfb8M+Ns+di9x5DDPPNmxSD51Ol/AQ1SLFDOf0AgXz890PX7N7ywGjZT5z5jwV\nPyhPufj93O3rDtgdcU5Sxu6IC9/26gZAl67t8Ijfz+XKlUarNTwyrkwZKypXqcjNW3fIkyc3+fLl\nBSBPntw0b9GIywFBRsmbGefq5HmbGTEvwLmzF6iQKHOXr6xxsHNNUsbBzpUe38U/ELdLG7w9DZmL\nFi2MRmPoQitXvjQVPyjPzRBDm278pOEUKJiPieNmGy3ra4Zr0zf14quvO2CX7O/Pzs6F73oZ9nOX\nru3wfFu9qFyBm7fucPt2GHU/r0Xu3IZ63qRpQ64keiCssQT6X6FUhVKUjG9zNu/clONO6bsJ37xz\nM1wOyrQxIudI98NIFUX5CNACD4CjwB+KomxVVfWJoiilgFjAE9ioKMrc+PfoCKx6yyaPY5hmxgbo\nheHhpf/2elq0xDDdS5tEuTdhmHrGCyirKEqD+JsGrx+YCuAOrAN+JH7UvaqqVxVFOQ3MVBRlsqqq\nuvj53jP99o5ep2frlLWM2jwZjVaD1y5XwoJv02VET0IuXMXP+TTfjO9LrjzmDF4+CoAHofdZ8uNc\nrCqVpt/sn9GrKhpF4ciK/YRlQeeJXqdnxeQVzLSZiUarwXGnI7eCbtF7ZG+CLwRz0ukklWtUZvKa\nyeQrmI96LevRe2RvBrUchNZUy4K9CwB49vgZC4ctzNKpY3Q6HX+Mm8+6nUvQaLXs3XaIq1eu89vv\nP3PR7zJuRz2pXqsqSzfOp0DBAjRr3YghY3+mY2Pjzhv3tmzDh0/miO1WNFoNmzbuJOByEFOnjObM\nWX9sbZ3YsGEHGzf8TUCAN1EPo+ndZ3DC+kFXfChQID9mZqZ06tgGa+vvePT4CePHDyMwMBjfk4bG\n+PIVG9mwYbtR8k4aO4tte1ej0WrYuXU/QYHXGD1+CP5+l3Cyd2OHzV6WrJyL9xl7oqNiGDxgNAAx\nMY9YvXwTdi47UVFxdfLCxdHQKJ4xdzxVqxke+fDnghVcv2b8zp3X+ceOms7eAxvQarVstdlN4OVg\nxk8aht/Zi9jbuWCzaRcr1y7ijL8LUVHRDPh+eKZk+S/e17qs0+kYN2YGu/evQ6PVss1mD1cCrzJu\n4lD8zl7Ewd6VrZt3s3z1Anz9nIiOiuHH/iPeud0NW5ZSpEghYmPjGDtqeop5So2Zf96EP1m+fTEa\nrZaD2225fuUGg8YOJMAvEA9Hb6rW+ojF6+dQoFB+Grf6gl/GDOTrJr2pULk8I6cNMXzOU1HYvGI7\nVwMzp4MvaWg9tyevptKWaShaDQ92uvAi6DaWo77j2fmrxDj5UqJ/Bwq2+hxVp0MX/YSQkX8nrF5l\n72xyfVAabV5zqvuu4+aYpTz2OPf298toXJ2OmeMWsHbnEjRaDfu2HY6vuz/F110vqtf6mH8S6u6X\n/Db2Jzo2Nsw3anNoNRUrlSNP3ty4+R1m0ohZHHNL283e/x5aT+SMFZRZNxO0GmL2OPLq6i2KDe3N\ni4vBPHE9SZG+ncnXvF78Pn5M+LjF795uZkbW6flz0j8s3jYPrUaL7U57bgSFMHD09wT6B+HtdJyP\nan7InHUzyF8wH1+0asDAUd/Tu/kPlKtcjrFzRyS0L7Ys3U5IsPGPxTqdjrGjp7PnwHq0Gi1bbfYQ\nGHiV8ROHce7cBRzsXNmyeTcr1yzktJ8zUVHRDHzH8cLCojjLV81Hq9Wg0Wg4sM8eR4fMuXBTdXrc\nJ2+ii81YFK2GgJ0ePAwKpf7IbkReuMENp7PU+L41ZRtVQx+r40XMUxxHGprKL2OecXatPT1tZ6Cq\nKiFu/oS4+mVKzsT0Oj0bp6xh3OapaLQa3He5EBp8m69Hfsv181c563yKXhP6YZ7HnKHLxwDwIOwe\niwbOwapSaXpP+h5VVVEUhSOrD3D7StZ1qL7NmKlzOXXuPNHRj2jRpTeDB/ShW8c2714xC+h1elZO\nXsEMmz/QaDU47XTiVtAtesW3mX2dTvLDxAGY5zFn3IrxANwLu8cfA2ZkcVA9L3YsI8+w2SgaDa+O\nOaIPv0mujn3R3Qwi7vwJdJdOY1K1NnmnrgZVz4u9a1CfPibujBcmH9Yk75RVoKrEBZwm7vzJd7+n\nkeh0OmaMX8C6Xf+g1WjZs93QHhoa3x5yPerJJ7WqsmzTgoRzytCxP2H9Zda07aeNm8em3cvRaDTs\n3naQ4CvXGT5uEBf8AnBx8GDn1gMsXj4TV9+DxEQ/YuiP4wCwWb+T+Uum4+C9B0VR2LP9IIEBhk/m\nmOc2p1GTekwaOdPoeTOjDZc7tzlNmjVk5DDjT1mXlt//7q0HWbB8Bk6++4mJesSInyYAhsE963Yt\nRdXriQy/y5jBUxK2O2nWaD6qVhmAZQvXJox0N1bmMaOmsf/gJrRaDTabDdcgEycN5+zZC9jbubB5\n005Wr12M33lXoqJi6N9vKAANGtZlxMhfiI2LQ6/XM3L4FB4+iKJ8+TJs3bESABOtlt27DuHsZJwb\nTplxri5eohg225YZ8pqYsGfXYVycjTfoQqfTMX70DHbtW4tGq2X7lr1cCbzK7xOG4nfuIkftXdlq\ns8dQl885EhUVw08/GDI3+OIzfp8wlLg4HXq9jtEjphIdFYOllQUjxwwi6Mo1XD0ND1Fdt2YLW4w0\nKEyn0zF61HT2HdiIVqthi80eAi8HM2HScM7F1wubTbtYvXYR5/xdiYqK5ofvhwFQv0FdRoz6mdjY\nOFS9nlEjpvLwQRQPH0Rx8IADnscOERen47z/JTau3/GOJOmn1+lZMnkp87fOQaPRYL/zKCFBN+k/\nuh9X/IM47uTDhzWr8MfaaeQrmI8GrerTf2Rf+rf4EQCL0hYUtyqOv895o2cTIrMoaZkfSVEUHW8e\n9KkAE1RVPRK/bBiG+dEBngC94+dFnwj0BW4Cd4CA+OlZ3IHRqqqejl+/PLAewyj5e0B/VVVv/cvr\nGwFbVVX3xJexVVW1evy2NgK2QAfghKqqKxP9DJ2AQfFfdhhuBjQEgoE+iaa5WYrhAaolEr1WAFiA\nYZqch8BzYIeqqksVRWka//N0eNd+TO/UMdktUv88uyOk2/UX78fHQdPjekz4uwu9R4rnKZjdEdLt\nWWzOezq5RZ60Pcj2fXH/Rc6bh7dMnvf7IZvJrTPNvoeU/le9Xuas4xvA/kI5az8PePoiuyOk2+VH\nWfcwRGOZWrhedkdIl5NKpnzYMlNtOmPc+ZmzStfav2V3hDTb1i7rp57JqLr7jDudSGaLU3Pe1AaP\nXuW840WRXAXeXeg9EvHMeNP4ZBUTjTa7I6SbVpPuCRuy1Stdzjsm1y5UMbsjpJvbHSeZA+U/2GrV\nO0f1X6amV9iWbPndp2lEu6qqbz3Kqqr6N/B3Kq/PAmal8nrTZN+HEP9w1TS+/n2yMtVTWZbi1qGq\nqoeA13PFV02+PFG5IcCQZK89An5+S3l3DCPhhRBCCCGEEEIIIYQQIsfK8b3s2Shn3fITQgghhBBC\nCCGEEEIIId4z0tEuhBBCCCGEEEIIIYQQQmSAdLQLIYQQQgghhBBCCCGE+J+hKEpbRVGuKIpyVVGU\ncf9S7mtFUVRFUeq+a5tpmqNdCCGEEEIIIYQQQgghxP9v+v+BR8gqiqIFlgGtgDvAKUVRDqmqGpCs\nXH5gKHAyLduVEe1CCCGEEEIIIYQQQggh/ld8DlxVVfW6qqqvgB1A51TK/QHMB16kZaPS0S6EEEII\nIYQQQgghhBDi/wVFUX5SFOV0oq+fkhUpBdxO9P2d+NcSb+NToIyqqrZpfV+ZOkYIIYQQQgghhBBC\nCCEE+uwOYASqqq4GVv9LkdQmyFETFiqKBvgT+D497ysj2oUQQgghhBBCCCGEEEL8r7gDlEn0fWkg\nLNH3+YHqgLuiKCFAfeDQux6IKh3tQgghhBBCCCGEEEIIIf5XnAIqK4pSQVEUM6AncOj1QlVVY1RV\nLaaqanlVVcsDJ4BOqqqe/reNSke7EEIIIYQQQgghhBBCiP8JqqrGAUOAo8BlYJeqqpcURZmhKEqn\n/7pdmaNdCCGEEEIIIYQQQgghxJuJyv+fU1XVDrBL9tqUt5RtmpZtSkd7FrIJO5HdEdIll4lpdkdI\nt0ZFP87uCOn2PN/L7I6QLpVyl8zuCOn2VP8quyOk2+VHt99d6D1S0CxPdkdIt4gXUdkdIV3av3iY\n3RHS7cGzR9kdId265LBmZeSznFWPAfQ5bB8DbIoNye4I6RL+POcdL3Kq/Wf/ye4I6TKo7tjsjpAu\n+UyeZneEdLn19G52R0i3AmZ5sztCuj2Ne57dEdJFr+a8855Wk/MmP3gem7Ou+TRKas9hfL891r3I\n7ghCvPeko10IIYQQQgiRrbrW/i27I6RbTutkF0IIIYQQmSvn3aYUQgghhBBCCCGEEEIIId4jMqJd\nCCGEEEIIIYQQQgghBPqcN7PRe0NGtAshhBBCCCGEEEIIIYQQGSAd7UIIIYQQQgghhBBCCCFEBkhH\nuxBCCCGEEEIIIYQQQgiRATJHuxBCCCGEEEIIIYQQQgj02R0gB5MR7UIIIYQQQgghhBBCCCFEBkhH\nuxBCCCGEEEIIIYQQQgiRAdLRLoQQQgghhBBCCCGEEEJkgMzRLoQQQgghhBBCCCGEEELmaM8AGdEu\nhBBCCCGEEEIIIYQQQmSAdLQLIYQQQgghhBBCCCGEEBkgHe3viTatm3LpoieBAd6MHfNriuVmZmZs\n27qCwABvjnsfply50gnLfh87hMAAby5d9KR1qyYJr69ZvYiwO/74nXNJsq1tW1dw+pQjp085cjXo\nBKdPOWY4f6tWTTjn58L5C+6MGjUo1fybNi/l/AV33D0OULasIX/z5o3wPnYYX18HvI8dpkmTBgDk\ny5cXnxN2CV83b51l/vwpGc6ZmrpN67DWfQ0bvNbxzeDuKZZXr1edpXb/YHfDlkbtGyVZNmDCD6x2\nXska11UMmv5LpuR7rUnzL3A9eQiPU7YMGvZDiuVmZqYsXTsfj1O2HHDcSukyVgCYmJiwaNlMjnrt\nxcXnAIOHD0hYZ8GS6ZwJdMfRe1+mZgf4rGldNnqsY7P3Bnr+2iPF8k/qfcJK+2U4htjT2PrLhNdr\nNazJqqMrEr7sr9ryRZuGmZ63ftPP2em1md3HttJnyHcplteqV4NNR1fjfcuFZtZNkiw7dtuFzU5r\n2ey0lgUbZ2VqzhYtG3P6rBPn/F0ZMfLnFMvNzMzYsGkJ5/xdcXHbS9mypQCoXacGXscP43X8MN4+\ntnTo2BqASpUrJLzudfwwt8P8GDT4e6PlbdLiC9xOHsLz9BEGDxuQYrmZmSnL1i3A8/QRDjq9qcdd\nvrbG3mN3wlfIfX+qVv+QvPnyJHndL9iTqbPHGi0vQLMWjfA6dYTjZx0YMnxgqplXrl/E8bMOHHHe\nQemyVgnLPq5WhcOO23D3OYTrsQPkymUGgKmpKQv+mob3aTu8fG2x7tTK6Jm9T9nh8y+ZV61fjM9Z\nB+ycd1AmWWZbx+14+BzG7djBhMzb9qzGxXs/Hj6Hmbd4KhpNxpoQrVs35eJFTy4HeDPmLee9rVtX\ncDnAm2PJzntjxw7hcoA3Fy960ir+vFelygcJ57bTpxx5cD+Qob8Zfvatic57wUY6771No2YNsD++\nh6Mn9/Hjb/1SLK9b/1P2OttwMcyHNh2aZ1qO1LRo2Rjfs46c8Xdh+FuOF+s2/c0Zfxec3PZQJv54\n8Vrp0pbcjvBnyFDD326uXGY4u+/Fy+cwx0/ZM27iMKPnNebxDWDwr/05ccoeH1971m34K6F+Z4aG\nzeqxz2sbB4/v4PshvVMsr12/Jlsd1+F7250W1k0TXrcsbcHWo+vY7rSB3e42dOvbOdMy5sTj29vU\nblKHlW6rWO25hq9Tac91GdiF5S4r+OfoUmZtn0XxUsWzJFd6TJq9mMbWPenSO3PblOlVrUktZrr8\nzWz3f2g3qEuK5a0GdGDG/7F331FRXI/fx9+zS1FjbwjYa6ygYkdBRFQQQbHE2EuiRmPvxt6Nmmg0\nahITa+wFUUGx967YwQLSQboNKTvPH7usLGBlN379Pfd1jueE3TvLh8nMvXfu3rnj+wszvJcwZvN0\niloW175X1KI4ozZMZfaRX5nl+wvFSht+v38J5x6Ag2NzLlz14dINX4aP+j7b+yYmxvz1z69cuuHL\noWM7tHVymbKWhETd5PgZT46f8WTxLzO120yeOgq/uycJCr+u97wtHJpy5MIejl3yZPDwfjnmXf7X\nAo5d8mT3oQ1YljEH1NciP6+Yhfep7Rw+t0vnOqbv993xPr0DnzM76Tcoe387t+xb2XLq0n7OXPVm\n6FvquFVrF3Pmqjdevlu0/U5Q13H7Dm3m2DlPjpzdo63j3DycOXJ2D75ndrNpxxqKFC2s18yOrVtw\n7cZR/G4dZ/SY7HWB+rr6N/xuHef4yT3atq+lgy2nz+7j4iVvTp/dp72uBvDwcOHCRW8uXznE7DkT\n9ZrXoVVzzl/x4dL1wwwf9V0OeY35859fuHT9MD5Ht+scx8GRfhw/vZfjp/fys+Y4zps3D/9uX8O5\ny96cvrCfqTPG6DUvfPo+rm9jxbkLBzh34QDnLxzEtcOb/sXvqxcSGHSZS5d99J43I/PV60e4cfMY\no96S+Z/1y7lx8xjHTux+k7l+Hc6c38+Z8/s5e+GATp8IQKFQcPqcF9t3/mWQ3ABNWjZk1+nN7Dm3\nhT7DemR7v25jKzYdXsuFkOM6dXKGr/Ln4+C13YyfO9JgGQVBn/Qy0C5J0vNM/+0sSdIDSZLKSpI0\nWJKk3prX+0qSZPH2T9GWWaGPTJk+01OSpPNZXlsnSVLnj/yctpIkXZIk6b4kSTckSdomSVJZfWRU\nKBQsXzaX9q49qW3Vkm7d3KlevYpOmf79uhMfn8jXNWz5dfmfzJ83BYDq1avQtasbdawdcGnfg9+W\nz9MOfmzYsB2X9tkrsm97DMGmgRM2DZzYs+cge/cezHX+pb/MoqN7X+rXa02XLh34+uvKOmX69O1K\nQkIidWrbs+K3tdoGPjY2ns6dB9CwYVu+/24Mf639BYDnz1/QpLGz9l9ISBienvpvtBQKBUPnDOWn\n3lP5zmEQLd3sKVtF93/r07BoloxewvG9x3Ver1G/OjVtajDY6QcGOQ6hqlVV6jSurfeMGTlnL5pM\nn65DcGzqTodO7ahSraJOmW49O5GYkIRdg/asXbWRidPVDZGLmxMmJsa0ae6Bi8M3fNuns7YTuWPL\nPvp0zf7FiCHyD58zjEm9ptC/5Xc4uNlTLst+jg6LZtHoxRzde0zn9Rvn/BjUZgiD2gxhbLfxJCcn\nc+XkVYPnHTtvBKN6TKC7fR+c3BwoX6WcTpmosGhmj1zA4T1Hsm3/OjmF3q0H0rv1QMb1nWLQnEuW\nzqBzp/40tGmDRxdXqmU593r36UJCQiJ1rRz4feU/zJw9AYB7dwOwb+5O86aueLj349flc1AqlTx8\nEEjzpq40b+qKna0br14ls99LP4OSCoWCOYum0KfrD7Rq4kb1dOozAAAgAElEQVQHj7cfxy1sXPhr\n1UYmzRgFwN6dB2hn14V2dl0YOXgyocHh3L3tz4vnL7Wvt7PrQlhIBN5eR3P69Z+ced7in+jReRB2\njVxx7+xM1WqVdMp07+VBYkISTeu15Y/f1/OT5oJAqVSy4o+FTBg9E/smHfBo34fU1DQARowdRMzT\nOGxtnGnRyJXzZy7rNfP8xVP5tvP3tGjkSsfOLtkyf9urMwkJiTSp15Y1v2/gpxljtZlX/rGI8aNn\nYNfElU6ZMn/fbxStbDti18SVYsWL4ureNlcZly+bi6trT+pYteSbt7R7CfGJVK9hy7LlfzIvU7vX\nrasbVtYOtM/U7gUEPNK2bQ0bteXly1fs9fQGoEeWdm9PLtu9d/1d0xaO57vuI2hv2xWXTk5UqlpB\np0xEWCSThs9k/+5DBsnwrmw/L51Bl04DaGzTFo8u7bPVF736dCExIZH6Vq1YtfIfZszW/dJq7sIp\nHPE9pf359esU3Fx60byJKy2auNLKsTk2Daz1llff9Zu5uRmDh/TBvrk7TRq2Q6lU4NHZVS95c8o/\nYd5ofuwxFg+7nrR1d6RC1fI6ZSJCo5gxYh4+WdqRp1Gx9HUdTPfW/ejt/D39hvWkuFkxg2T80uq3\nd/0tQ+YMYXqf6fzQagh2HVpQpkoZnTKP7jxmlMtIfmwzjDMHztJvcvZJC5+bu3NrVi+d87lj6JAU\nCnrMGsivfecytfUoGnawxbxyaZ0ywXcDmeM6gRntxnDV+zxdJvXSvjdg6Y8c+sOTqY4jmes2iWcx\niQbN+yWcexk5Fy6ZTjeP72jWwJlOndtnO/969FbXcQ2tW7N65TqmzxynfS8oMJiWtm60tHVj7Kjp\n2tcP+RzDqeVHXeZ+cN6ZCyfSr9sw2jTzwLVTWypX1e3Dde3hTlLCMxwauvH36s1MmK7+8tXZzRET\nUxPatehKh1Y96N7HA8sy5lT9uhLdenWio1MvXOy64eDUgvIV9XJ5rc089+cp9OwymJaNO+Du4UyV\nnOq4xCRs67fjz1UbmDJjNKCu45avWcDEMbNwaOpGl/Z9SU1NQ6lUMmv+RLq49qO1bSfu3Q2g33f6\n+4Ig47q6k3tfbOo5vfO62qp2S1bqXFfH0aXzQBo1bMeg78by59qlABQtWpg58ybR3qUHDWzaULJk\ncezt9TNpSaFQsGDJNL7pPJBmDV3o6PG24ziJhnWdWP37OqbNHKt9LygwmJbN3WnZ3J1xmY7jlb/9\nTdMG7XBo3pGGjerRyrGFXvJmZP7UfXz3jj/Nm3WgaWMX3N37sHz5XJRKJQCbN+7C3b2v3nJmzbxk\n6Uw8OvajQf02dM6xT9SVhIQkrOs4sHLF39o+0d27AdjZumHbpD2d3Puy7Lc52swAQ4b2I8D/kUFy\nZ2SfMG80w3uMpYtdL9rkUCdHaurkQzlcVwMMnjCQa+dvGCyjkDNZ+vL/fS56ndEuSVIr4DegrSzL\nwbIsr5ZleYPm7b7AOwfa9U2SpMJAPaCwJEkV3lf+HZ9TC/Xf1UeW5a9lWbYGNgPlcyj70Q+Ybdig\nLo8eBREYGExqairbt3vSwbWNTpkOrk5s3LgDgF27DuDQ0lbzehu2b/ckJSWFoKAQHj0KomGDugCc\nPnORuPiEd/7uzp1d2brN82Mj67CxsebxoycEBYWQmprKzp1etG+v+01pexcnNm/aBcCePQe1jbuf\n3x0iI6IBdSNgamqKiYnu7LJKlcpTokQxzp69lKucOalmXZXwoHAigyNJS03jxL6TNHFqrFMmKjSa\nwPtBqGRZ53VZljExNcHIxAhjE2OMjJXEx7x7f38q63q1CAoMJuRJGKmpaXjt8aF1u5Y6ZVq3s2fX\n1n0AHNznS7MWjbQ58+XLh1KpJE8eU1JTUnn2TP3d2KXzV0mIN+wFD8DX1tUICwonQrOfj3uepKmT\nbgcvKjSKx/cCkVXyWz4FWrg059LxK7xOfm3QvDXqfk1oUBjhwRGkpabh63mMFm2a6ZSJCI3k4b3H\n78xraPVtrHj8+M25t3vnflxcHHXKOLs48u9m9R0Le/d4Y2evnt3y6lUy6enpAOTJY4osZ/877O2b\nEvg4mJCQcL3kta5fm6DAYIKfhKqP493eOGU5jp2cW7Iz4zj2fHMcZ+bm0Q7PXdkHSstXLEuxEkW5\ndF5/X8TUrV+boMcZmVPx3OVNG2fdWchtnR3YvmUvAPs9D9PcTl2H2Dk0497tAO7e9gcgPj4RlUr9\nWJlvenRk+S9/AupzNC5Of3VH3fp1CMyUee+ug9kyt3F2YPsWT03mQ9hqMts7NOPubf9MmRO0mZ8/\newGoZ6aZmBhDDsfMh8ra7m3b7olrlnbP9S3tnqtrG7a9pd3L4OBgy+PHTwgODsv2uzt3dmVbLtu9\nt6lTrybBgSGEaurqg3t8adVW946XsJAIAu4+/M/rjoz64om2vjiAc5b6op2LI1s27wHAc4+Ptr4A\ncG7vyJPAEO7fe6CzzYsXLwEwNjbC2Ng4x7okN3n1Xb8pjYzImzcPSqWSvHnzEhkRpZe8WdWqW53Q\noFDCgsNJS03jkOcR7Nvo3hUXERrJg3uPtOdYhrTUNFJTUgEwMTVGyuXdI2/zJdZvb1PVuioRQeFE\nafoZp7xO0ThLf+7W+Zva/oP/9fsUNy+e00d9VjbWtSlUsMDnjqGjgnVlop9EEhMSTXpqGpe8zmLt\n1ECnjP/5O6QkpwDw6PoDipRSD06bVy6NQqng7pmbALx+mawtZyhfwrkHUM+mDoGZ6uQ9uw7QLlud\n3IqtW9R18r69PjTPVCe/zdXLfkRFPdV7Xqt6tXgSGKK9Ftm/5xCt29nrlHFsZ8+urV4AeO87QtPm\nDQF1dyFfvjxvrkVSU3n+7AWVqlbgxtVbJGvq7IvnruLk0jLrr/5k6jou5E0dt/sgbZyz9DvbObBD\n0x864HlY2x+yc2jKvTvZ6zhJkpAkiXxf5QWgQIGviIrU3/62sbHKdl3t0l73riAXl9aZrqu9tdfV\nN/3u5nhdXb5CWR4+CCQmJg6A48fP4paLyRKZ1atfh6DHT3gSpOlz7j5AO5dWOmXaOTuw7V/1cey1\n9xDN7d59HL96lczZ0xcBSE1N5abfXcwtzfSSF3K3j3X6F6amOl3hs2cvEW+g9s4mS59o18792TO3\nd2TLZnXmvR+Y2cKiFG3atmT9um0GyQ1Qs251QoLCCNNcVx/2PIpdDnXyw3uPUOXQN/66TlWKFS/K\nhZOG/9JeEPRFb70HSZKaA38CLrIsP9K8NkOSpLGa2eM2wGbNbPC8kiQ1kCTpnCRJfpqZ4hm9SgtJ\nknw0s+IXZfp8J0mSzkuSdE2SpB2SJOXXvB4kSdJMzeu3JEn6OlMsD8AL2Ap8kyWyoyRJpyVJCpAk\nqb3msy5KklQz0+88IUlSfWACME+W5XsZ78myvE+W5VOZys2TJOkk8NH3TVtYliIk9M1AVmhYBBYW\npd5aJj09ncTEJIoVK4KFRQ7bWupu+zbNbRsRFf2Uhw8DPzaybjYLM0LD3mQIC4vA3MLsrWXS09NJ\nSnpGsWJFdMq4u7fjpt8dUlJ0O+BdunZg1879ucr4NsVKFedp+JvOUUxEDMVLfdjMlXvX7uN3/iZb\nrmxmy9XNXD15jZCHIQbJWcrcjIiwN4MBEeFRlDIvma1MeLi6THp6Os+SnlOkaGEO7vPl5cuXXL57\nlPN+h/lj5XoSE5IMkvNtipsX52nEm/38NPIpxc0/foZQyw722e4sMIQSpUoQnem4iI54SgnzD7/V\n2cTUhH+81/CX1++0aGv7/g0+kYWFGWGhEdqfw8Iis5175haltGXS09NJSnxGUc25V9/GiguXvTl3\n8SCjRkzVdsIydOrcnp07vfSWt5R5ScLDIrU/R4RHYWZu9tYymY/jzFw7tsVzt3e2z3fzcMZrj37v\nfCllbkaYTubInM+9TJmTkp5RtGhhKlUuh4zMll1/cPjkTn4Yrp49WbCQurmbMOVHDp/cyR/rfqF4\nCf3NmDPPYT+bZ9nP5uZmhIe9OS6eaTJXrFweGdiy608On9zF0OG6y/ts2fUntx+e4fmzF3h5fvqM\nbAvLUoSG6rYblh/Y7llaZN82a7vXrasb27btzfZ7bW0bEa2Hdu9tzEqV0KmrIyOiMPuIusOQzLPU\nF+E51BeZ6xR1ffGcosWKkC9fXkaMGsTC+b9l+1yFQsGpc/sICLzIiWNnuHrFTy95DVG/RURE8dvy\nv7h97zQBj86TlPSMY8fO6CVvViVKlSAyLFr7c3TEU0qW+vBjwcyiJNuOruPg1d2sX7GZmKhYvWf8\nEuu3tylWqhhPw2O0P8dExFDsHTORnbo5cfX4FYPn+r+giFlR4jPt2/iIWIqYFX1r+eZdHbh1Qr1s\niVlFc14mveSH1eOYduBnOk/qZdDBa/gyzj3QtMOhb86/8PAc6jjzLHVy0jOKFlXXcWXLlebY6b3s\nO7iJxk1sDJIxs1LmJYkI170Wydq+mZmXJCKHPpz3viO8fJnMhTu+nLnhzZ8rN5CYkETAvUc0bFKP\nwkUKkSdvHuwdbTG3+LDr2A/L/Kavk5G5VNZ+p0XJbHVckaKFqVipPMgym3f+gc+JHQzR1HFpaWlM\nGjObo2f2cu3eCapUq8SWjbv0ltnCohShYbptX7ZxAQszbZn09HQS33Nd/fhREFWrVaJsWUuUSiWu\nrq2xLK2f+Y/mFrrtSHhY9j6nuq1513G8B88DG2ncpH62zy9YqABO7Vpy+uT5bO99qtzuY5sG1ly+\ncoiLl30YMWJKtusnQzC3KEWoTh8uAousfXsLM20Z7X7OyGxjxcXLPpy/5M3I4T9pMy9YNJVpUxZk\n+9JRn0qWKkFUtjr5w77oliSJUdOHsWz274aKJwgGoa+ejingCbjLsnw/65uyLO8ErgA9NLPB04Ft\nwAhZlq0AR+CVprg10A2oDXSTJKmMJEnFgZ8AR1mW62k+a3SmXxGjeX0VMDbT692BLZp/3bPEKg/Y\nAS7AakmS8qAekO8KIEmSOWAhy/JVoCZw7T37oLAsy3ayLC/J/KIkSd9LknRFkqQrKtWLHDeUpOz3\nNGSdEZZzmQ/b9m26dXPXy6y+D8rwnjLVq1dh9pyJ/Pjj5GzlOnd2ZfuOfbnOmZMcYn3wJE2L8uaU\nqVyGHg178W2Dnlg1taJWo1r6DZghx5xZj5Gcy1jXq4UqXUXDmo7Y1mvHd0P7UKacZfbC/7GPnfVY\ntGRRKnxdnssnDX9hnNO+/JjZu+4NutKv3SCmDZ3NqJnDsCxnmJt53lYv6JbJvl3Gvr96xY/GDdrR\n0q4jo8cM1lmr2NjYGGeXVuzdo78lNj69rntTxrp+bV69Sibg3sNs5Tp0asu+XdkH4HMjxzwfUkaW\nUSqNaNi4HkO/G49b2560a++IbYvGGCmVWJY25/LF6zjZdebq5RtMnzMu22foN/OHtSlGSiWNGtdj\n6HfjcGvbQ5s5Q3eP77Cq1gITUxOd1/WSUU/tnrGxMe3bO7FzV/YvaL/p5p7ru7jeKRdtsqHlpq2e\nOGUEq1b+o529nplKpaJF0w7UrGZLPRsrqteokq2M/vK+N+4767fChQvi4uJInVr2VKvclHz58tG1\nm2HWYM5N/wwgKjyabq364takG+27tqVo8SLv3eZjfYn121t9wPGSwb5jSyrXqcKuNfobHPs/7SOO\n5cbuzSlXpxKH/lDXs0qlkioNvmb73PXM6TCBEmXNaNbZ3pBpv4hzD3LRDiITFRmNdU17HJq7M3Xy\nfNasXUL+Al8ZJOebMNlfyl4n5/w3WdWriSo9nSa1nLCr78LAH3pRppwljx4Esmb5OjbsWsW67Su5\nfyeA9PQ0/UX+kOunt/xhSiMlDRrXY9j343Fv14t2Lq2wbdEIIyMjevfvRhu7ztSrbs+9OwH8mMO6\n5J+eOfd95erVqzBrzgSG/6heci8hIYmRI6ayfuMKDh/ZzpMnYaSn6Wc/5yZvVGQ0dWu2xKF5R6ZO\nWcDqv3SPY6VSyR9rl/LX6o08CQrVS97cZga4cvkGDWzaYNfcjTFjfzDos17e5Mn+2oceywBXrvjR\nqEFb7Fu4M2bsEExNTWjb1oGYp7HcuHHbEJEzB3tbrPfq0rcjZ49eICo8+v2FBb1T/R/497noa6A9\nFTgHZH+yXc6qARGyLF8GkGU5SZbljNr+qCzLibIsJwN3gXJAY6AGcFaSpBtAH83rGTKe5HgVzXIu\nkiSZAZWBM7IsBwBpmiVgMmyXZVkly/ID4DHwNbAdyHh6UldgR9bgkiQV08zKD5AkKfOgfo7328iy\n/IcsyzayLNsoFDl3gMJCIyiT6Vvl0pbmRGS5lTlzGaVSSaFCBYmLiycsLIdtw99/G7RSqaSjezu9\nDGCHhUVS2vJNBktLc+1taxnCM5VRKpUULFhAeyuxhWUptmxdw3cDRxMYGKyzXe3a1TEyUnLjumEa\ngJiIGEpYvJmNUdy8OLEfOHOlaZum3L9+n+SXySS/TObK8StUr/v1+zf8BJHhUTq3zJlbmGW7TTEi\nPAoLzUwYpVJJgYL5SYhPxK2zMyeOnSUtLY3YmDiuXrxOHeua/JdiImJ0ZoSXKFWC2Mi4j/oMe9cW\nnPE5R3qa4WcNREc8pWSm46KkeQmeRsa8YwtdGbOfwoMjuHbuBlVr6WfgKauwsEgsS5trf7a0LJVt\nGYTwTGWUSiUFCxXIdltjgP8jXrx8RY0a1bSvtXayw+/GHZ5G628mV0R4lM7MY3MLM6Ijo99aJvNx\nnKFDp5yXjalesypKpZJbfnf1lledJxJLncyliIrImjlSJ3PBggWIj08kIjyS82cvExeXwKtXyRzz\nPUVtqxrExSXw8sVLDnqp1yH02nuI2nVq6C1zeA77OVudHB6JheWb46JAwQLExycQHh6lk/mo7ynq\nWOlme/06hcPex2jr/OkP8gwLjaB0ad12I/wD273QsOzbZm732rZtyfXrt4iO1j1nlUol7u7t2GGg\nL24BoiKiderqUuZmRH9E3WFI4VnqCwvLUjm21br1RX7i4xKwaWDFzNnj8btzgiE/9GX02CF8N6iX\nzrZJic84c/qi3tZRNUT9Zt+yGU+CQoiNiSMtLQ2vfYdo1LieXvJmFR0RTSnLN7PDS5qX4GnUxx8L\nMVGxPPYPpG4jK33GA77M+u1tYiNiKGHxZoZccfPixOXQflnZWtNtWDdmD5hFWor+BvT+L4uPjKVI\npn1bxLwYCdHx2cpVb1Ybl2EerBi4QLtv4yNjCbkbRExINKp0FdcPX6JsrYrZttWnL+HcA007XPrN\n+WdhkUOdHJ6ljiuoruNSUlK1dZ3fjTsEBQZTufInr5L6QSLDo3Vm3Kv7cE+zlInCPIc+XAePdpw8\nek5zLRLP1Ys3qG2trhe2b95LB4dv+cZ1AAnxiQQ90r0WzA11n/JNO6K+fnp3v/NNHRfFhbNXiI9L\nIPlVMsd8T1PLqgY1a6uv9Z4Eqe9i9trrQ/1G+nk2Cajv0ittqdv2ZRsXCIvUllEqlRTKcl3979Y1\nfD9wjM51tffBo7S060irlh48ePCYhw+D9JI3PEy3HbGwNCMy2z6OxNIyy3EcrzmONcvc3tQcx5Uy\nHcdLl83m8aMg1qxar5esGXK7jzP4+z/i5YuX1KhZDUMLD4uktE4fzpyIyOz1Reks9UXWzAH+j3jx\n4iU1alSjUZP6tHNpxa27p/hn/XJa2DXRruuvT9ERTzH7xDq5tk1NuvbvxL5L2xk5/Qecu7Rl2ORB\nes8oCPqmr4F2FeqB6QaSJGWfkpydRPZJMxkyL76cDhhpyvvKsmyt+VdDluUBOWyTUR7Us+KLAIGS\nJAWhHoDPvHxM1t8vy7IcBsRKklRHs/1WzXt3UK/1jizLsZpZ+X8A+TNtn/N09Q9w+coNKleuQPny\nZTA2NqZrVze89us+fNBr/2F69VJ/B+Dh4cLxE2e1r3ft6qZef618GSpXrsCly+9/yrxjq+b4+z/U\n3saVG1ev+lGpcnnKlSuNsbExnTu7cuCAr06ZAwd96dHTA4COHZ05efIcAIUKFWT3rn+YPm0RFy5k\nX1e5S5cO7Nihv6UrsvL3C8CyvAVmZcwwMjbCvoMdF3wvfNC2T8OfUqdRbRRKBUojJbUb1ybYQEvH\n+F2/Q4WK5ShT1hJjYyNcO7bF1/uETpkjPifw+KYDAM4dWnPutHpN+7DQCO0aiXnz5aWuTR0ePTDM\nsglvc9/PH8sKlpQqUwojYyNautlxzvfjbgFs6daS456GXzYG4N4Nf8pUKI25Jm9rNwdOHz73QdsW\nKJQfYxNjAAoVLUSdBrUIDAgySM5rV29SqdKbc69T5/YcPKj7INCDB4/ybY9OALh3bMcpza2X5cqV\n1j4Ip0wZC6pUqcCT4DezRTp3cWWnns89v2u3dY/jTu3w9TmhU8bX+wSdM45jtzfHMahnl7i4OeG1\nO/vyMG4ezuzLYTmZ3Lpx7TYVKpWjTDlLjI2NcfNoxyFv3ePwkPdxunZ3B6C9mxNnTqnXlTxx9Cw1\nalbTrgfduFkDAvzVM/EP+5zQnpe2do31+hCiG9duUbFSOcpqMrt7OHM4S+bD3sfp2t1Nk7kNZ09d\n0GQ+Q/VMmZs0a0CA/yPyfZWPkmbqL5+USiWtWtvx8MHjT86Ytd3r1tWN/Vnavf1vaff27z9Mt3e0\ne+q7tbIvG9NKj+3e29y6fpdyFctiWdYCY2MjnDu25tihU+/f8D+gri/KUVZbX7jgnaW+8Dl4lO49\nOgLg1rEtp06qjwtnp+5Y1bTHqqY9q35fx9LFq/hzzUaKFS+qXSokTx5T7Fs25UHApx8X2fPqt34L\nCQnHpqE1efPmAcDOvin+BnoA2J0b9ylToQwWZcwxMjaijZsjJw+d/aBtS5qXwDSPeoZcgUIFsGpQ\nhyd6HHzK8CXWb28T4BeARQVLbX+uhWsLLvpe1ClTsWZFhs0fxuwBs0iMNfzzaf6vCPJ7iFl5c4qX\nLonS2IiGrs3w89VdK7dMzQr0mjeI3wYu4Fnsm+UJA/0eka/QV+QvWhCA6k1rEfFAfzNTc/IlnHsA\n16/eomLF8to6uaOHCz7Z6uRjfNNdXSd3cG+rXT6jWLEiKDRL8JQrX4aKlcoTFGSYa5AMN6/foXzF\nspTWtG/tO7bhSJY+3FGfk3h8o37AdLsOjpw/rT5OwkMjadpcva5/3nx5sLapw+MHQeq/RXPHgIVl\nKdq0d2BfDn28T6Wu48pq+p3GuHXKoT/kc5wumv6Qi5sTZzV13MmjZ6lesyp5tHWcDQ/8HxEZEUWV\napW0S3K0sG/KQ3/9tHsAV6/ezHZdffCA7gMiDx48kum6uh0nNcdFoUIF2LXrb2bkcF1dQrOEV+HC\nBfnu+556W5P7+rVbVKj05jh27+SCz8FjOmV8Dh6j27fq49jVvQ1nNH1O3eO4NBUrldd+gTHpp5EU\nLJSfKRPn6SVnZrnZx7r9C0uqVK1I8BPD1mkZmStm6hN5dG6fPfOBo3Tvoc7s/tbMFlSpWpEnwaHM\nnP4z1as2o3aNFvTrM5xTJ8/z3YDR6NvdG/cpU6G0tk52cmvFqUMftmzf1KGzaW/TmQ4Nu/LrzN85\nuMOHFfPW6D2jIOjbRz+4821kWX6pWev8tCRJUbIsr81S5BmQsQ77fdRrsTeQZfmyZn32V7zdBWCl\nJEmVZVl+KElSPqC0Zqb623RH/VDW8wCah6H6ol6CBqCLJEnrgQpARcBf8/pWYDxQSJblW5rXFgF7\nJEm6kGmd9nzv+N0fJT09nREjf+LggX9RKhSsW7+Nu3cDmDF9LFeu+rF/vy9//7OV9euWc//uGeLj\nE/i25w+A+kEnO3d6ccvvOGnp6QwfMUW7xtamjSuxa9GE4sWLEvT4CjNnLeafdervDrp2ddPb7fPp\n6emMGT0Nz30bUCqVbNiwnXv3HvDT1FFcu3aLgweOsH7ddv5au5Sbt04QH59An94/AjBocG8qVirH\nxEnDmThpOAAdXHvx9Kl6FlInDxc6deynl5w5UaWrWDl1FfM2zUGhVHJ422GeBATTe0wvAm4GcMH3\nIlWtqjLtz6kUKJSfxo6N6D26J987Dub0gTNYNbVije8qZBmunLzCxSMX3/9LP0F6ejrTJsxjw45V\nKJVKtv+7lwf+jxg98Qdu3rjLEZ8TbNu0h19WzePk5f0kJCQybOB4ADas3cri32bje3Y3kiSx419P\n7t9VP8xu+R8LadLMhiLFCnPhli+/LPidbZoH4emTKl3Fb1NXsHDzPBQKBd7bDvEk4Al9x/bG3y+A\n874XqGZVlZl/TSd/oQI0ad2YPqN7MaDV9wCYlTajpEUJ/M7f1Hu2nKSnp7N4yjKW/fszCqWC/Vu9\nCQwI4rtx/bjv58/pw+eoblWNhWvnUKBwfmxbN+G7sX35tmU/ylcpx4SFY5BVKiSFgg0r/yXowROD\n5Rw7Zia7965DqVSwaeNO7t97wOSfRnL92i28Dx5l4/rt/PHXEq77HSM+PoH+fdWPkWjcxIZRYwaR\nmpqGrFIxZtR04mLVM9Ty5s1Dy5bNGDl8it7zTh0/j407V6NUKtm2eQ8B9x8xetJQbl2/g6/PCbZt\n2s2vq+dz6soBEuLfHMcAjZrWJyI8MscObXv3NvTp9oNe82ZknjxuLlt2/YlSqWDrpj0E3H/IuMnD\n8Lt+h8Pex9mycRe/rVnIuWs+JMQnMLi/+manxMQk1qxcj/ex7ciyzFHfUxw9rB50nTtjKb+tWcCs\n+ROJjYln1FD97Wt15jls2fUXSqWCLZt243//IeMn/8iN67c57H2cfzfuZMWahZy/5kNCfCKD+o/J\nlHkdPsd2aDMfOXyS4iWKsWHLSkxMTVAqlJw5fYH1f3/6RVpGu3cgS7s3ffpYrmZq99atW849TbvX\nI1O7t2OnFzdzaPfy5s2DY6sW/PDDhGy/U71uuwGXjdH8XbMnLmLttuUolEp2/buPh/6P+XHCIG7f\nuMfxQ6eoZV2DFesWUbBQQVo62TJs/CBcW3QzaK6MbPSyiakAACAASURBVOPHzGTX3n9QKpVs3riD\n+/ceMOmnEdy4dltbX6z+awlX/Y4SH5/AgL4j3/mZpcxK8PsfP6NUKlAoFOzZfZBDPvr5QtQQ9Vtc\nbDyee304dXYfaWnp3PS7w7q/t74nyafnXzh5KSu3LEWhVLBv6wEeBwQyeNwA7vrd59Ths9Sw+pol\nf8+jYOECtGjdjMHjBtDFvhcVqpRj9PRhmqWSYOPqLTy8r7+BnMwZv7T67W1U6SpWT13FrI2zUSgV\n+G7zJTggmB6je/Lg1gMu+V6k/5QB5MmXh4mrJgHqCROzB8wyeLaPMW76Ai5fv0lCQhKt3Hvyw4Be\neGR5UPR/TZWu4t9pfzFyw08olArObj9G+INQ3EZ1I+jWI/yOXKHLpF7kyZeHwb+r25K4sBhWfLcQ\nWaVix9wNjN08HSR4cvsxp7Yeec9vzJ0v4dzLyDlx3Cx27FmLQqnk34078b//kIlThnPj2m18vI+x\necMOfv/jZy7d8CUhPpHv+o0CoEmzBkycMoK0tHRU6emMHTlNe/ff9Fnj8OjiSr58ebl57xSbNuxg\nUQ7P1/iUvDMmLmT9jt9RKBTs+NeTB/6PGTlxCLdu3OWoz0m2bd7L0t/ncOySJ4kJSQz/biIAG//e\nxqLlM/E5sxNJkti55c21yO//LKZw0cKkpaYxffwCkhKf5Tpr5sw/jZ/Lv7v+QKFUaPudYycNw+/G\nHXy9j7N14y6Wr17AmaveJMQn8sOAN3XcH7+v5+DRbcjIHPM9ra3jfln0O7sPrCc1LY2wkAhG/fAh\n8ww/PPOY0dPZu28DSqWCjRt25HBdvY2/1v6C363jxMcn0ld7Xd2HipXKMWHSj0yYpH7NzbU3T5/G\nsujnadSuXR2ABfOX6+1ZNenp6UwaO4vtu/9CoVSyZdMu/O8/ZMLk4dy4fptD3sfYvHGn+ji+fpj4\n+ES+7//mOJ4webj6OFalM3bUdBLiEzG3MGP0uCEE+D/i2Cn1denaPzexacNOvWX+1H3cpGkDxowZ\nTGpaGiqVilEjpxKruX76Z90ymrdoTLFiRfB/cI65c35lw/rtess8bswM9niu12a+f+8BU34ayTVN\nn2jD+m388ddSbtw8Rnx8Iv36DNdktmHU6DeZR4+cpr3m+y+kp6fz8+Rf+G3LEpTaOjmIQeMGcC9T\nnfzz33MpWLgAzVs35ftx/elm3/s/yygI+ibpY+1QSZKey7Kc8XDSMsApYCRQF3guy/JiSZI8gHmo\nB9SbALWA34C8mtccgc6AjSzLwzSftR9YLMvyCUmSHICFqNeDB/hJluV9mtnqNrIsx0iSZAMsBvoC\nZ1EPxmv/QEmSrgFDNP/iUT+g1QwYLcvyfk0ZMyAMmC3L8sxM27oAM1B/WRALBAPTZVkOkCTpBDBW\nluV3Lh5tZGL5v7FQ6wcyNTL+3BE+mm2x6p87wke7/zLsc0f4KJXz6u8hRf+VF6qU9xf6H3MvybAz\nk/StkInevnv8z6SqDL8MkT5lXWf9SxD78r998LI+VCpsmGcqGErUy//uYklfVF/gsVypgPn7C/0P\niXj1ccuz/S+oX9CwS18Ywp5ruR/A/K8NsRn//kL/Q669jnx/of8hwS++vLWEC5oYeH13A3id/mX1\n7RNfZ3/Gyf+6vMaGX3tc316lflnHhSLHB4P9b6ta8PM/6+1jXYk4/eXt6P8BK8r0/PI67FkMC9n0\nWf7f62VGe8Ygu+a/Q1DPEgf1A1IzXt8FZH7q0GXUa69ntk7zL2Ob9pn++xjQIIffXT7Tf18B7DU/\nZqsBNA9MBXjrtGNZlqPIYb/IsnwAOPCWbexzel0QBEEQBEEQBEEQBEEQBEH4v09fa7QLgiAIgiAI\ngiAIgiAIgiAIwv+XxEC7IAiCIAiCIAiCIAiCIAiCIOSC3h6GKgiCIAiCIAiCIAiCIAiCIHy5vvgF\n2j8jMaNdEARBEARBEARBEARBEARBEHJBDLQLgiAIgiAIgiAIgiAIgiAIQi6IgXZBEARBEARBEARB\nEARBEARByAWxRrsgCIIgCIIgCIIgCIIgCIKASvrcCb5cYka7IAiCIAiCIAiCIAiCIAiCIOSCGGgX\nBEEQBEEQBEEQBEEQBEEQhFwQA+2CIAiCIAiCIAiCIAiCIAiCkAtijXZBEARBEARBEARBEARBEAQB\n1ecO8AUTA+3/oXEWdp87wkd5/QWeWk1fKz93hI82NC3wc0f4KEHJMZ87wkermtfsc0f4aD2K1//c\nET5KOvLnjvDRGqeafu4IH8W1TsjnjvDR2t4o/rkjfLTTy1p/7ggfpfKgbZ87wkeLS37+uSN8tO1F\nvvrcET7KbpPynzvCRxvc9unnjvD/hVVXFn3uCB9tts3Uzx3hgxUzLv+5I3y0ParIzx3ho5kp83/u\nCB9lV8Tlzx3ho+U3yfO5I3w0Y8WXNRaQLn954y2bC31Z554gfA5ioF0QBEEQBEEQBOEjDbEZ/7kj\nfJQvcZBdEARBEAThSyIG2gVBEARBEARBEARBEARBEIQvcH2L/x3iYaiCIAiCIAiCIAiCIAiCIAiC\nkAtioF0QBEEQBEEQBEEQBEEQBEEQckEMtAuCIAiCIAiCIAiCIAiCIAhCLog12gVBEARBEARBEARB\nEARBEATkzx3gCyZmtAuCIAiCIAiCIAiCIAiCIAhCLoiBdkEQBEEQBEEQBEEQBEEQBEHIBTHQLgiC\nIAiCIAiCIAiCIAiCIAi5INZoFwRBEARBEARBEARBEARBEFBJnzvBl0vMaBcEQRAEQRAEQRAEQRAE\nQRCEXBAD7YIgCIIgCIIgCIIgCIIgCIKQC2KgXRAEQRAEQRAEQRAEQRAEQRByQazR/j+uql0d2k/r\njUKp4PK245xc5aXzfsMerWjSqzUqlYqUF6/ZM+kvoh+Gka9wfr5dNYLSdSpxbecp9k1f959l/trO\nCvdpfVAoFVzYdoxjq/bpvG83wJlG3zigSkvnedwzto1fTXxYDADtJ35L9ZZ1AfD9bTc39p83eF6z\nlnWwntULSakg8N8T+K/wyrGcpUtDmvw1gqNtfyLeL5CSLWpRe8o3KIyNUKWmcXPWvzw9e9dgOVu2\nsmXOwikolQo2b9jJb7/8qfO+iYkxK9YspI51TeLjEvi+32hCgsMoU9aS05cO8OhBIABXr/gxftQM\nAHbv34BZqRIkv0oGoFvHAcTExOktcwuHpkydNxalQsm2TXtYs3xdtsyLf59NrTrViY9PYPjAiYSF\nRGBsbMScJT9R27o6KpXM7Ck/c/HsVQD+2baCEmbFURopuXLhOtPHL0ClUuktc4b69vUZPGMwCqUC\nny0+7Ph9h877tRrVYtD0QVSoXoEFQxdw5uAZ7Xv9J/WnQasGAGxZtoVTXqf0ni8nNeys6DKtH5JS\nwbltRzm8ylPnfYcBLjT7phWqtHSexSWxafwq4sJiqNqkJh5T+2jLlapkwd8/LsPv8GWD5q1pZ03X\naf1QKBWc2XaUQ6v26rzvOKC9Nu/zuCTWj/+dOE1dUcSiOL0XDKaIRTFkGVb0m0ds6FOD5gWwtK9D\no1m9kBQKArac4NZK3fqiWi8HqvdR18lpL5I5O34tiQ/CURgrabpwAMXrVECWVVyctonI8/cMnhfA\nuH5Dvhr8I5JCQbLPAV7t+DdbGZPmLcnXsy/IMmmPH/F80WyUFSuTf9hopHz5QKXi5daNpJw6bvC8\nTVo2ZOysESiUCvb+u5/1KzbrvF+3sRVjZg2ncvWKTBk8k6MHTgBQqrQZP6+di0KhwMjYiO1/72LX\nBs8cfoP+nX0QzqIDV1DJMh3rV6Z/i5o670ckvGDq7vM8e5WCSpYZ7mRN86qW3AqNYbbnJXUhWWaw\nQx0capQxWE77VrbMmj8RhVLJlo27WPnrXzrvm5gYs2zVfGpr2pEh/ccQGhIOQPWaVVm4dDr5C+RH\nJatwcejG69cpdOjYlh/HfI9SoeSo7ynmTl+it7ytW9uxePF0lEol69ZtZfHiVVnymrB27VLq1q1N\nXFw8PXsOIzg4FAcHW2bPnoiJiTEpKalMnjyPkyfPAeDpuZ5SpUpiZGTE2bOXGDlyqkHaEIB8tjYU\nnzQYlEqSdnqT8Nd2nfcLuLem+NiBpEXHApC4eR9Ju3wAqHTrICkPggBIC48mYtgMg2TMqoJdHVpN\nV/eJbm49wcUs/U7rHg7U7d0aVbqK1JfJHJq0ltgH4RQsXZwBRxcR9ygCgIjrDzk85R+D51XWtCFP\n18FICiUpZ7xJObQ9Wxmj+i0wbd8TAFXoY16tXQCAaacBGNVuBJJE2r1rvN62Ktu2hlDTzprumrbv\n9LajeGdp+1oPaE/zb1qhSlPxLC6Jf8av1LZ9RS2K02fBEIpaFEOWZZb9R23fu/w0bymnzl6iaJHC\n7N20+rNmyVDZrg7O09TH8bVtJzid5Ti26dGKRtrrp2T2TVrL04dhVLKtResJ36A0NiI9NY1D8/4l\n8Lzh+vaZlbOrg90MdeY7W09w5XfdzLV7OlCnd2tkzbl3dOJa4h6o6+fiX5fBYX5/TArkRVbJbHWd\nRvrrVIPmbWjfgGEzf0CpVHBgizf/rtyq836dRrUZNuMHKlWvyKyhczh54LT2vZIWJRn382hKWpRA\nlmFi78lEhkYZNC+AlV1d+k4fiEKp4NhWXzxX7dZ532VgBxy+aU16WjpJcUmsHvcbMWFPKVejAgPn\nDiJv/nyo0lXsWbGD8/vP6jVbGyd7li6dhVKh4O9/trDo55U675uYmLDun2XU07R33XsM4cmTUAAm\njB9Gv77fkK5SMWrUVA77nsTU1JQTx3ZhYmqKkZGS3bsPMHOWun0+cWw3+QvkB6BkiWJcvnIDj84D\nPjl7y1a2zF4wWXuNuiKHvsVvqxdSx7oG8XEJDOo/mpDgN32Ln3+ZSYEC+VGpVLR16IJCoeDPdb9S\nrkIZVOkqDvscZ+7MpZ+cLyetHFswf9FPKJVKNq7fzq9L12TJbMKqP3/G2roWcXHx9O8zgpDgMO37\npUubc/6KDwvnLWfF8rWYmppw4NAWTE1NUBoZsW+vDwvmLtNrZsfWLVi4aBpKpYL167fzyxLd+tbE\nxIQ1fy6mbt1axMUl0Lf3jwQHh1G/fh2WrZgHgCRJzJ+7jP1eh7XbKRQKTp7xJCI8iq6dB+o1c4Z8\ntvUxmzIYFAoSd/oQ96fudXXBjo6UGDeQtCh1W5ew2YvEnYfeZPwqH+UPruH5kXNEz/5v2moBDNM7\n/v/DBw20S5JUDDiq+bEUkA5k9OoayrKckqV8UaCrLMurNT9XBm4B/oApcBEYKMtyWq7/AvXnHwAK\nyrLcPNNrm4CdsizvffuW2T7HGZgJFACSgXvAOFmWQ9+znREQI8ty4U/J/9bPVUh0mNWPtT3nkxQZ\ny9B9c7jne43oh28qeT/Pc1zarP5fU92xHi5Te/JPn4Wkvk7Fd8lOzKqVplRVw12055S506z+rO45\nl8TIWEbtm8cd36tEZcocdjeIX1wnk5qcQtOerWk/qQcbhy2jesu6WNYszxLnCRiZGDN02zTunbjB\n6+evDBdYIVF3Xl9Od5vPy4g4WnnPJvzwNZ4FhOkUM/oqD5UHtiH26kPtaylxzzjbezHJUQkUrFaa\n5lsmcKDej4aJqVCwYMk0urr3JzwsikPHd3Do4DEC/B9py3zbuzMJCUk0rtsGdw9nps4cw/f9RgPw\nJDCYVs075vjZP3w3Dr/rtw2SecbCCfTp/AOR4VHs8d3EUZ+TPAwI1Jbp0sOdxIQkHBq60b6jExOm\nj2D4wIl069UJAOcW3ShWvAh/b1uBu2NPZFnmxwETeP78BQAr//kZZzdH9u85nGOG3GQfOmcok7+d\nTExEDMv2L+Oi70WCHwRry0SHRbNk9BI8BnnobNvAoQGValViaJuhGJsYs2jnIq4cv8LL5y/1mjEr\nSSHRbdYAlvecQ0JkLBP2zeem7xUiM517oXeDWOA6kdTkFJr3bE3HST1ZO+xXAs7fYb7zeADyFfqK\nmSd/4+4pPwPnVdB91gB+7Tmb+Mg4JmnyRjx8U90G3w3kpOsEUpNTaNHTCY9Jvfhz2C8A9Fs6DO8V\nu7l35iam+fIYbKBMN7NE47l9ONR9AS8j4nA9OIvgw1dJ1FzoAjzecx7/jccAKNO6Hg2n98S35yKq\nftsSgL2Ok8hTrCCtN43Dy3kayLJhQysU5B86ksTJY1DFPKXwsjWkXDxLevCTN0UsLMnXrQeJY4Yi\nP3+OVEjdlMmvk3m2eC6q8DAURYtR+Lc/ib96GfnFcwPGVTBh3miGdhtFVMRTNnj/yanDZwkMCNKW\niQyNYsaIefQa8o3OtjFRsfR3HUJqSip58+Vl24n1nDx0hpioWIPlBUhXqZjvdZnVfR0wK5iPHqt9\nsPu6NJVKFtKW+fPkbZxqlaVrw6o8ik5k2MbjeI+xpHLJwvw7uC1GSgVPn72i68oDtKhmiZFS/zcc\nKhQK5v48he4dvyMiPIqDx7Zx2Ps4DzK1I917eZCYmIRt/XZ06NSOKTNGM2TAWJRKJcvXLGDE4Enc\nve1PkSKFSE1No0iRQvw0ayxt7bsQFxvPr7/Pw7ZFI86cuqiXvL/+OhsXlx6EhUVy5sw+9u8/wv37\nD7Rl+vbtRnx8IrVq2dGliytz506kV69hxMbG07lzfyIioqlRoypeXhupVKkRAD17DuXZM/UxvGXL\najw8XNixI+cv2HP5B1Dip6GEDZxEWlQMZbb9xovjF0h9FKxT7Jn3KWLmrsy2ufw6hZBOP+g/1ztI\nCgnH2X3Y3mMBzyLj6L1vFg+PXCU2Ux131/M8Nzar67jKjvVo+VNPdvZZBEDCkyjWO0/5LwOTt/tQ\nXvw6CTk+hq8m/UbazQuoIt7sY0VJC0zbduPFz6Ph5XOkAurzUlmxBspKNXkxazAA+cYvQVm1DukB\nNw0cWUGPWQNZ2nMW8ZFx/LRvATdyaPvmuE4gJTkF+55OdJnUizWatm/A0h85sGIXdzVtn/wftH3v\n4+7cmm89OjB59uLPHQVQH8ftZ/Vlfc/5JEXGMWjfbO77XuNppv7QLc9zXNFcP1VzrEfbqT3Y2GcR\nL+KfsXnAYp5FJ1Cyaml6b5jA4saG6dtnzWw/pw97eizgeUQc33jN4rHvVe1AOoD/3vPc2qQ+9yq0\nrkfzqT3x7L0ISamgzbIhHBq5mph7weQpnB9Vql4usd9KoVAwYs6PjP12Ak8jnrL6wErOHj7Hkyz9\n5AWjF9FtUNds209eNoGNyzdz9fQ18ubLg0pl4L4Q6nOv/+xBzO0xndjIWObv+5krRy4R9uDNuRd0\n5zGT2o8hJTmF1j3b0mNSH5YNW0zKq9esHLWMyKAIipQswvwDS/A7dYOXSS/0kk2hULB82VzaOncn\nNDSCC+cP4rX/MPfuvWnv+vfrTnx8Il/XsKVr1w7MnzeFb3sMoXr1KnTt6kYdawcsLMw45L2V6jWb\n8/r1axyduvLixUuMjIw4dWIPPj7HuXjpGvYOnbSfu33bH+zz+vTrKIVCwfzFU+nqPoCI8Ch8jm/n\nsPdx3WvUXp1JSEikSb22uHVy5qcZYxnUfzRKpZKVfyxi2KAJmr5FYVJT0zA1NWHVir85e/oSxsbG\n7PD8GwfH5hw7cvodST4u889LZ9CxQx/CwyI5dmo33geP4n//zXV+rz5dSExIpL5VKzp1dmHG7PEM\n6DNC+/7chVM44vtmItXr1ym4ufTS7m9v360cOXySK5dv6C3zkqUzcXPtTVhYJCdO7+XggSM6mXv3\n6UpCQhLWdRzw6NyembMn0K/PcO7eDcDO1o309HTMSpXg3IUDeB88Snp6OgBDhvYjwP8RBTRfvuid\nQoHZtKGE9p9MalQM5XYs4/mxi6Rk6w+dfOsgevERvXh1+ZZh8gmCAXzQlZwsy7GyLFvLsmwNrAZ+\nyfg56yC7RlFgcJbX/DXb1wYqAB7ZtvoEmi8BagNmkiSVzcXnWAG/Aj1lWf4aqAtsA8rlUPY/uROg\njHVlYp9EER8STXpqOn5e56nuVF+nTOZBaJN8psiaQZvUV695csWfNAPPZsiqrHVlYp5EEqfJfN3r\nHLWcbHTKPDx/l9Rk9WHz5PoDCpcqCkCpKpY8ungPVbqKlFevCb8XzNd2VgbNW7RuJZ4HRfEi+Cly\najohnhewaFM/W7maEzoTsHI/qtdvDveE209IjkoAIMk/FIWpMQoTwxwa9erXIfBxME+CQklNTWXv\n7oO0dWmlU6atcyu2/6v+Xslr7yFs7ZoYJMuHsqpXiyeBoYQ8CSM1NY39ew7h2M5ep4xjO3t2b90P\ngPe+ozRprp4FXrlaRc6dVs/yjI2JJynxGbWtawBoB9mNjIwwNjE2yDhlVeuqhAeFExkcSVpqGif3\nnaSxU2OdMtGh0QTdD9KecxnKVinLrYu3UKWreP3qNYF3A6lvn/2Y0rfy1pV5+iSSWM25d9XrHFZO\nDXTKBJy/oz33AjOde5nVdW7MnRPXteUMpYJ1ZaKfRBITEk16ahpXvM5ilaWu0M0boM1rXrk0SqWS\ne2fUAyKvXyYbPC9A8bqVeBYUxfPgp6hS03nseYGyWeqL1Ex1slE+U+1AeuGqloSfuQNAcmwSKUkv\nKW5VweCZjapWJz08DFVkBKSl8frkMUwa2+qUydPWlVdee5Cfqwcf5UR1vaYKC0UVrh6YUMXFokqI\nRypUCEOqWbc6IUFhhAVHkJaaxmHPo9i10c0bERrJw3uPsl2Yp6WmkZqibvNMTI1RKP6b1fFuh8ZS\nplgBShctgLGRkja1y3HiXohOGQl4kazO9jw5hRIF8gKQ18RIO6iekpaOhGSwnHXr1ybocQjBT9Tt\niOfug7RxbqlTxqmdAzu2qO8COOB5GFs7db1n59CUe3cCuHvbH4D4+ERUKhVly5fh8cMg4mLjATh9\n8jzOHZz0krdBA2sePQoiKCiE1NRUduzwon371jpl2rdvzebNuwDYvfsg9vbNAPDzu0NERDQAd+8G\nYGpqiomJCYB2kN3IyAhjY+Nsdbi+5KldjdTgcNJCIyE1jefeJ8jv8Hnb5fcxt65EQlAUiSHqOu6e\n1wUqt9at41Iy1XHG+UwBww+QvY2yQjVU0eHIMZGQnkbqlRMYWenuY2PbdqSc8IKXmvrtWaLmHRnJ\n2ASMjMDIGElphJwUb/DMWdu+S15nsc7SVvufv0OKpk17dP0BRUoVA9Rtn0Kp4G6mti/lP2j73sfG\nujaFChb43DG0SltXIu5JFPEhT0lPTeeW1wW+fs/1U8ZhHHnnCc+i1W1gdEAoRqbGKA3Ut8/MzLoS\niUFRJGn6FwFeF6jo9I5zL++b/kW5FrWJuRdCzD31oFVywnNkAw9cf21djbCgcCI0bfUxzxM0c2qm\nUyYyNIrH9wKzfRlUrkpZlEolV09fA+DVy2ReJ782aF6AytZViAqKIDokivTUNM55naFB60Y6Ze6c\nv609px5c96eYufrciwgMJzJIfbdOfHQ8STGJFCxaUG/ZGjaoy6NHQQQGBpOamsr27Z50cG2jU6aD\nqxMbN6pnAe/adQCHlraa19uwfbsnKSkpBAWF8OhREA0bqO8Of/FCPcnH2NgIoxzau/z5v6KlfTM8\nPX0+OXtdzTVqRt9i766DtHF20CnTxtmB7Zq+xX7PQ9q+hb1DM+7e9s/Ut0hApVLx6lUyZzXXgamp\nqdy6eRdzi1KfnDGr+jZWPH78hCea/sXunQdwdnHUKdPOxZEtm/cA4LnHBzv7N22Lc3tHngSGcD/T\nFyGgu7/13b+w0WTO6BPt2rkflyx9Ipf2jmzR9In27vHG3r4pAK9eJWsH1fOYmupcO1tYlKJN25as\nX7dNb1mzylOnKqnB4aRq+kPPDp4kf6vG799Qw7RmZZTFivDi7DWDZRQEfcv1VagkSeMlSbqt+Zfx\nlf8CoJokSTckSVqQubxmFvtlwFKz/UBJknZLkrRfkqRASZKGSJI0TpKk65IknZMkqbCm3ChJku5K\nkuSnma2eoTOwF/WgeLcs8dpIknRakqQASZLaaT7niiRJ1TLlP6MZZJ/4/9g776ioru9vP3cGVBBB\nelewJPbeNWIvsdfEWJOYxCQae4slxhZjS9doii3GFjWxYAFUsCui2EWlqDD0Zldm7vvHjDADg8LX\nGdDfe561WMuZe+7MZ67n7r3PufvsA8yRZfmaTqcsy/K/siwf1Ws3T5KkEGCkJEkVJUk6KUnSaWDW\ny15HY9i62pMRl5OJl6lKxc4178RYk8HtmRD8HZ2mvMfOWWvNIaXA2Lk6kK6nOT0fzc9o3L81Vw5p\nn/TGXrlF1VZ1sCxVgtL2ZajUtBpldQGOubByc+BhbI7eh6pUrNzsDdqUrVEeKw9HVIFn8/0czy6N\nSL8Yg+aJeTJI3DxciYtVZb+Oi43Hzd3VoI27uwuxujZqtZq7mXdxcNBmppYr70Xg4W1s372Oxk0N\nA/cffplP0OHtjJ34qUk1u7o7o4qLz34dH5eIq7uL4e9yd0YVG6+n+R72DmW5eimCdp38UCqVeJXz\noEbtqrh75vzeVZt/4dTVQO7fu8+eHYEm1Q3g5OZEUlzOUuxkVTKObgXri1FXomjQqgElS5XE1t6W\nWk1r4ezhbHKNuSnr6kCa3r2Xpkp57r3XrH8bLh3Km2XRoFtzQneYdjmsMfLqTaWsa/7XuHn/tlw6\npL0HXSq48yDzPiN+ncC03QvpM1VbysXcWLvZcz8up7TSA1UqpXPZC4AqQ9vR5+gSGk5/l5MztTY5\n9fItynWsh6RUYOPtjGNNH0p7mNe+ASicnNAkJWa/1iQnoXB0Mmij9PRC6emN3eKfsftuGZb1G+X5\nHIs3qoCFJRpVXJ5jpsTFzZmE2By9iaokXNycnnOGIa4eLmwIWs3uM1tZ8/N6s2ezAyRmPsTNzjpH\ng501iXcNV2KNaFOL3eFRdFi0jZHrDjGlS85DpQu3k+n94y76/ryb6d0bmSWbHcDN3dCPqOIS8vgR\nNw8X4vRscmbmXewdylKhog/IMuv/WcneQ1v4GS4gIQAAIABJREFU9IsPAIiOvEWlyr54eXugVCrp\n+HZbPDxNMxj28HDjzp0cvbGxKjxzfba2TZyBXkdHw3uyV6+3CQ+/xJMnOROSO3as5datMO7du8+2\nbf4m0ZsbpasjT+Nz/EhWfDJKl7x92aZDc7y3L8ftu+lYuOX4CqlECbw2/4TXhu8p3bZoJuht3Oy5\nq8qxcXdVqZQxYuPqDmnHRyFL8Jv6LkFf5cSddt7ODPWfy4BN0/Bq+Gae80yNVNYRTVrONZbTklGU\nNbzGClcvFK6eWE9civXk71FW19576sgrZF0Lp8zCDZRZtIGsS2fQxBs+IDMH9q4OpMUlZ79OU6Vg\n/xxf/Vb/NlzQ+T7XCu48yHzAZ79OZObuRfQtIt/3ulHG1SHP+MnWNW8/bjS4PWOCl9JhygB2z1qT\n53i1zo1QXYpBbabYXh8bN3vu6sUX91Sp2BjRXGtIO4YeXkKLL98lWHfvla3ghoxMz3WTGLB7LvVH\ndDG7Xmd3J5JUOb46KT4J5wKO2bwreHEv8x6zf/uK3/b+yojpHxfJg3EHNwdSVDn3XooqBXsjCSfP\naP1OO84dyjuxV7F2ZSxKWJAQE2/krP8ND083bt/Jia/uxKrwyDWxrN9GrVaTkZGJo6M9Hh5GztX5\nSoVCQejp/ahizxMUFMKp04Zj2Z49O3Pg4NHsB9D/C+7uOXEDaGML9zxj1Jz4Q3+MWqGSDzKwYetv\n7A/eyudf5C1fY2tXhg6dWnM42HTlZN09XIm9Yziudvcw1Oyh10atVpOZcQ8HR3usra0YPfYTvv3m\npzyfq1AoCDm2g4iokxw6cIQzoaZbHeyeKyaKi1Xhkfs6e7hmt3kWEznoYqIGDWpz8vRejp/aw5gv\npmdPvC9YOIOZ08xThvUZFq5OPFUZxkMWRsZ8Zdq3wOe/ZXj8MA2LZ7G/JOEy+SOSFv2ep71A8Crz\nUl5NkqRGwECgEdAU+EySpFpoJ62v6TLep+Q6xwpoCOzTe7s62knyJsC3QJosy3WBM8AgXZtJQB1Z\nlmsDI/XOHQBs0P0NyCXRG/ADugErJUkqiXZCvr9OixfgKMtyuE7Dix6T2cqy3FKW5e+Bn4AfZFlu\nSE4ZnTxIkvSxbnI/9NzdG/k1y+/kPG8ZezJ6Yl0Ai/3GsnfBBtqM6lm47zAxRiTn+zS3fs8WeNeq\nwMGV2iXbEYfPc+XgWb7YNptBP44iOuw6GrWZl8QaSx7U1ytJ1P56EOdnrTfSUIvtG57UnP4uYZP+\nML2+HBl5yX1djfYXSIhPpF71NrR7qzdfTVvA8t8XY1OmNACffTSBVs26073zIJo0a0C/d3uYULPR\nzlAAzTJb1v9HvCqRfwP/Yvq8CYSdCs8OCADe7/85Tap3oESJEtlZ8CbF6PUu2KlhIWGEHgxlyb9L\nmPzzZK6GXUWdpX7xiS9LQa63jkY936J8rQoErjTcP8HWuSweb5Yze9kY4MX3nh6NdXr36/QqlUoq\nN6zKP/PW8k33KTiVc6FZ31bm06rDWJ82JvnqmkC2Nh9P6LyN1B6ttcnXNwZry83smUPjrweRFHod\nuSj6RQEypCWlEqWnFxmTR3N3wWxsxkxEKp2zfFSyd8Bm4jTufbfA/KVuCt6NjZIQl8iAtsPo2fRd\nuvbvhINT3okKUyMbMQ65f8be89F0r1eR/RN78/PgVkzfeiw7I7+mtxPbvujK+k868UfIJR4/NU+/\nKIh/NppRL8soLZQ0bFKPkR9PomfnwXTu0pYWLRuTkZHJ1AlzWP7nErb7r+XOrViyskwzKVUgvS+I\nk6pWrczcuVMYOXKqQZvu3Yfg69uQkiVLZGd8mRzjjtvg1f2DJ4huN5TbvT7lwYmzuMyfkH0suu0g\n7vQfRfzEBThNGYGFt7t5dOph7P/f2P13dm0gv7UcT/CCjTTVxZ33E9P5tekY1rw9nQNz1tP1x88o\nYWNldsV5ySVYoUTh4smDJRN5+Ps3WA0eA1alkZw9ULh7c3fKQO5Ofg9lldooK9cws14KHNsDNOn5\nFuVrVWTfSm0mqNb3VWHzvDXM7T4Z53KuNC8C3/e6UdCxyKl1AXzvN479Czbil2v85FzZkw5T3mXH\nl+aL7Q0oYHxxfm0ga94az9FvNtLwC61mhVKJR4M32PvFMrb0mU3Fjg3wbl4978mmFVwgvcZQWiip\n2agmy+esZESXz3Av506n/qZZCfU8jPs3421b9PKjYs1K7Fix3eD9si72jPxuDMsn/GTSbOUX+bL8\n2zz/XI1GQ4OGHSjv24CGDepSvbrhA9B3+/dg46YCV9k1itHvp2DaLZRKGjepx+cfTaRHp4F07tqO\nFi1zMp2VSiW//r6Y31f8xa2Y51byfXnNBRyjTpk2muW/rMrOXtdHo9HQsll3qr/ZgnoNalO1WmUT\nas77XkFjOIDQ0HAaN+xEq5Y9GT/hU0qWLEGnTm1ITkrh3DnTl5B9Ibku972DJ4lsO4zoHp9x/9hZ\n3BaMB6Dse125H3yarPjkvJ8hMDvy/4G/4uJlHx+/BWyVZfmBLMt30WaWt8in7ZuSJJ0DUoAbsixf\n0jt2QJbl+7IsJwD3gGfFMi8APrp/XwL+kiRpIPAUQJIkT6AccEKW5cuAUpKkKnqfu1mWZY0uS/02\nUBnYDPTTHX9H99oASZJcdNn41yVJGqN3SH+Xl6ZoJ+0B1uXzm5FleaUsyw1kWW5Qp0yl/JoZJTM+\nFTu9jEdbdwcyE/Nf1np+53GqtW+Q7/GiID0+lbJ6msvmo7ly8xq0G9mLP4YvMsgUCfzlX5a8PYUV\ng+cjSRLJUao855qSh6pUrDxz9Fq5O/BQVw4GwMKmFLZVvPHbNp3Op77HoV4lmq0ej72u5IOVuwNN\n/xzL6S9+5X5MYp7PNxWq2AQ8PHMG2R6ebsTHG36fKi4BT10bpVJJGdsypKWl8+TJU9LStL/p/LlL\nREfdpmIlrf54XTbK/Xv32bZlF3Xr1zKZ5vi4RINlfm4eLiTEJ+Vto8u60Gq2IT0tA7VazbzpS+jW\negAjBo/D1q4M0bnquD15/ISgvcF5ytGYgmRVskEWupO7EymFyIzd+NNGRnYaybSB00CCuCjzZgED\npMenYK9379m7O5Jh5N57s3lNOo3sxfLhC8nKlaVVv2tTwvedQlMEE8Dp8am59DqQnph3I94qzWvS\neWRvlg3/NltvWnwKty5HkXw7EY1aw7n9pylXw/xlWO6rUintkZP9ZO3uwIOE/G2yfmkZWa3h1Kz1\n7OgwjaAPvqOEnTUZUabLhsoPTXISCueclSQKJ2c0KYbBqjo5iSfHj4BajSYhHvWd2yg9vQCQrK2x\nm/0tD9b8QdZV828Il6hKwtUzR6+LuzNJCYUPrpMTUrh5LZq6jc1bfgzA1daa+IycAVdCxoPs0jDP\n2H7mJh1qaKvb1S7nzOMsDekPDJfKV3Cxw6qEBTcS0zEHqjhDP+Lu4UqCET/ioWeTbW3LkJaWgSou\ngRNHQ0lLTefRw0ccCDhMjdracl4Bew/Rrf0AunccyM0b0URFGtrq/5XY2Hi8vHL0enq6ExeXkKuN\nCi8vDwO9qanpuvZubNq0kuHDxxEVlVfT48eP2bUrgG7dzDPBo45PxlIvQ93CzQl1oqEf0WTchafa\nkkKZW/ZQsnrOoFydpLWHWXfieXjqPCWrVjSLTn3uxqdSxj3HxpVxd+Dec2zclR0nqKwrb6F+ksWj\ndG1WZMLFaNJjEnHwNd1Sf2PI6cko7PVWAdg7oUk3vMZyWjJZ4cdBo0ZOSUCTcAeFiyeWdZuhjrwK\njx/B40dkXQxF6VvVrHpB67/sPXKy7u3dHUk34qurNq9Jl5F9+Hn4AgPfd/tydLbvO7v/FOVqVDC7\n5tcNY+Onu8+xqxd3Hqeq3vjJ1s2BASvGsm3cr6TdMl9sr889VSpl9OILG3cH7j9nzHdtxwkq6u69\ne6pUYk9e5VHaPbIePSH6YDjONXzMqjdJlYSz3ipVZzdnkuMLFicnqZK5cekGqlsq1GoNR/YdpXIN\n001I5kdKfAqO7jn3nqO7I2kJeePOms1r0XtkXxYOn28QJ1vZWDFl1XQ2LV7P9bMRJtUWe0eFt86X\nAXh5uqNSJeTbRqlUYmdnS2pqGrGxRs7N5SszMjIJDjlGxw6tst9zcLCnYcO6+PsH8TLE6cUNoI0t\n4lWJudrEZ8cf+mPUuLgEjh89TWpqOg8fPiIoIIRautgCYPEPXxMZGcNvy027Yj8uNh5Pr1zj6tya\n9doolUps7WxIS02nQcPafD1nEuGXDvHpZ8MYN+FTPvpksMG5mRl3OXL4JG3btTSpZi8Dze6o4vNe\nZy99zXox0TMirt3k/v0HVKv2Jo2b1qdzl7ZcuBzCqjU/0tKvKb/9YdpNZwGyEpKxdDeMh7Jyx0Pp\nd5F18VDGlr2U0sVDVnWqUnZgNyoErcZ50nBse7TDadz7JtcoEJial51oL0wx0Wc12isBfrqNR5+h\nP9rU6L3WkLNha0e09eEbAaGSJCnRTpQ7AlGSJEWjnXTX3x0t90MMWZblGOCeJEnVdOc/myy/BNTT\nNUrUaf0D0N8VQn/HE7M/JLkTfhMnHzfsvZxRWiqp3a0pVwLOGLRx9MlxbG+2qUtytPknbp7H7fCb\nOPu44aDTXLdbMy7m0uxZ3Yd+8z/ij+GLuJeSmf2+pJCwLqu93O5VyuFepRzXDpt3U6q0c5HY+Lph\n7e2MZKnEu0cTVPty9GbdfcjO6iPY02gMexqNITXsBseGLSEtPApLW2uar5vAxW82kXLatAFXbs6G\nXaBCxfKUK++JpaUlPXu/zT7/AwZt9vkfoP972uyWbj07ciTkBACOjvbZSzLL+3hRoWJ5YqJvo1Qq\ns0vLWFhY0L5TK65eMd3vOH/2Ej4VvPEq54GlpQVde3UkaG+wQZugvcH0frcrAJ27t+X44dMAlLIq\nhZV1KQCa+zUmS63mRkQU1qWtcHbVBslKpZJW7VsQeT3aZJqfEREegYePB67erlhYWuDX3Y8TAScK\ndK5CoaBMWW2tUp8qPvhW9eVMyJkXnPXyxITfxMXHHUfdvVe/WzPOB4QatPGq7sN78z9i+fCFBvfe\nMxp0b07oTvOXjQGIDr+h0+uC0tKCBt2aE55Lr3d1HwbN/5hlw7/lrp7e6PCbWNuVxkZXH7NKsxqo\nrpsu0yU/ks9FYuvrho23MwpLJRV6NOH2fsOFULa+Ocs4vdvVIVM3ma4sVQILq5IAeLxVA02WxmAT\nVXORFXEVpYcXClc3sLCgpF8bnpww/D9+cvwIlrW19TwlWzuUnt6oVXFgYUGZGXN5FLSPJ0cOmV0r\nwOVzV/H29cLD2x0LSws69GhLyL4jBTrXxd2ZkqW0dbjL2NlQu2HNPA/ozEF1T0dupdwlNu0eT7PU\n7LsQg18VL4M27mWtOXlT2xciEzN4kqXGvnRJYtPukaVbuRWXfo+Y5Ew8ypY2i85zYRfxrVgO73Ja\nP9Kj99vs33PQoM3+vQfpN0C7sqlLjw4c1W1qGhx0lKrV36CUVSmUSiVNmjfI3kTV0Uk7OWRnZ8vQ\nD99lw9p/TKI3NDScSpV8KV/eG0tLS/r168bu3QEGbXbvDmTgQO2WP717v01w8LFsLdu2rWLmzIUc\nP55jV0qXtsbNTTs5pFQq6dSpNdf0NmwzJY8uXsOyvCcWnq5gaYFN51bcP2joR5ROORNrpVs34anu\nIYXC1gYsLbX/LmtLqXrV82waZg5U4ZHY+7php7NxVbs14UaAoY2z98mxcRXb1CFNF3daOZRBUmiH\nBXbeztj7upJu5klKdfQ1FC6eSI6uoLTAskErssINr/HT8GMo39Q+cJNK26Jw8UJOVqFJTcLijVqg\nUIBCicUbNdHEm/8aR4ffwNXHHSed72vUrTnhAacN2nhX92Xw/E/4afgCA98Xlcv3VS0i3/e6ERse\niYOPG2V18VDNbk24mmss4qDXj99oU4cUXT8uZWvNoFUTCFy4iVtnzBvb65MQHklZXzdsdffeG92a\nEJnr3iurp9m3bR3SdZpjQs7jVKUcFqVKICkVeDapQur1WMzJtfBrePl64ubthoWlBW16tOJYwLEC\nnXv13DVs7Gywc9Du+VKvWR1irse84KyX52b4ddx83XH21t57zbq1IDTglEEbn+q+DP/mMxZ+OJ/M\nlIzs95WWFoxfOZWQrYc44V+w31kYToeeo1IlX3x8tP6uf/8e7NxluEHpzl37GTxYmyvYp08XDh46\nmv1+//49KFGiBD4+3lSq5Mup02dxcnLAzk5rK0qVKkXbNm8Z+Lu+fbqy2z+Qx49frj7+udxj1D5G\nYos9B+mviy269ujIUd0Y9VDQEapWfxMrXWzRtHnD7E1UJ08bTRnbMsyY8s1L6TNG2JnzVKxYnnLl\nvbC0tKR33y7syfXAYa9/EAMG9gKgR69OhARrNb/dYQC1q7eidvVWLF+2mqWLl/PbinU4Ojlga6cd\n/5UqVZJWrZtxPSLSZJrPnDlPhYo+lNdp7tO3K/67Dcun+u8OYoAuJurZqzPBunI75ctr97YC8Pb2\noPIbFYi5dYevv1pE1TeaU7NaS94f+gUhwcf56MNxJtP8jEcXIrAs74GlLh4q87Yf9w7kioecc1ag\n2rRpwpOb2lJuqokLiWwzlMi2w0ha+DuZ/wWSvHSVyTUKBKbmZXd3CQFWSJK0CFACPdBOXt8FjO6K\nI8tynCRJU4GpQIEKY+om1b1kWT4gSdIRtOVqrNGWimkny/JpXbvKwC5yaqb309Vzr4y2jMyzHSs2\n6b6/pC4THmAhsFmSpFPP6rTrviO/XYZOoC1Bs1Gnx+Ro1Bp2zFzNB2unICkVhG4+ROL1WNqN7Uvs\nhUiuBIbRdGgHKjWvgTori4cZ99kyPmen5klHfqCkjRVKSwuqdajPn4MXkHjDvIGXRq1h28xVfLz2\nSxRKBac2HyTh+h06je3H7QuRXAo8Q7epAylpXZKhy7SLBdJik/nzo8UoLS0YuWUWoN2kaP3Yn81e\nOkZWazj35Wre2jAZSakgemMwmRGxVJvYh7TwKFT7868mVPGDDtj4ulJ1TC+qjtE64sPvLuCxkQnM\nl0WtVjN1whw2bvsDpVLBhr+2cu3qDSZ9OYrwsxfZt+cgf6/7h59XLuTE2X2kp2XwyQdaR9mkeUMm\nfTkKdZYatUbNpLGzSE/LwNraio3b/8DSwgKFUsHhQ8f5a/UWk2r+esq3rN7yCwqFgn/+3sH1a5GM\nmTKCC+cuE7Q3hM3r/2XJsjkcOPUf6ekZjP5Iu7zf0cme1Vt+QaORSVAlMv7TGQBYWVux8q/vKFGi\nBAqlghOHT/P3atNM6uijUWtYPmM5c/+ai1KpZP+m/dyKuMXg8YOJOB/ByYCTvFH7DWb8NgMbOxsa\nt2vMoHGDGNFuBEpLJYu3Lgbgwb0HLPpikflLIOk0b5r5JyPXTkOhVHB880FU1+/QdWx/Yi7c5ELg\nGXpPHURJ61IMX6btG2mxyfz60UIAHLycsXd34voJ82ctP9O7ceYfjNbpParT223sO8RcuMn5wFD6\nTB1MSetSfLxMu3wwNTaZZR99i6zRsHXeOsaun4kkScRcjOTwxpfLyCkIslrDielr6PD3JCSFguub\ngkmPiKXuhD4kh0dxOyCMqsM64P5WdTRZap5k3OfwmBUAWDnZ0uHvycgaDQ/i0wj5YvkLvs1EaNTc\nW/49dnMXg1LBo/3+qG9FYz34A7IirvLk5DGenjlFiXoNKbtiDag13P9jOfLdTEq2bo9ljdooythS\nql0nAO4uXYA6spBl0AqBWq1m0Zff8dOGJSiVCnZs3E1kRDSfTPyQK+FXCdl/lGq1q7Doz3nYli3D\nW+2b8fHED3in1RB8K5dnzFcjkWUZSZL469cN3LxqukFOflgoFUzp2oBP1xxAo5HpUa8ilVzLsiwo\nnGoejrSq6sW4TvWZ/d8J1h+7CpLE172bIkkSZ2MS+TPkMhZKBQoJpnZtiH3pUmbRqVarmT5pHn9v\nXYlCqWDT+u1EXL3JhKkjCT93iYA9B9m4bis//rqAI2f2kJ6WwWcfakuZZGRksnLZGvyDNiEjcyDg\nMEH7QwCYvWAq1XTL0b9btJzIm6aZMFGr1YwdO5OdO9eiVCpZs2YzV65cZ8aMcYSFnWf37kBWr97E\nn39+x8WLwaSlpTN4sLay4IgRQ6lY0YcpU0YxZYp2+6Bu3QYjSRL//PM7JUqUQKlUEhx8jN9+++t5\nMl7iB2hImvcLHr/NR1IoyNy+nyc3YnAYOYRHlyJ4cPAEZQf3wLp1U8hSo864S8KXSwAoUaEczrO+\nAI0MCom03zbxtAgm2mW1hsCZa+i3dhKSUsGFzcGkXI+lxbg+xJ+P4kZgGHWHdsCnRXXUT9U8zrzP\n7nFaG+fduAotxvVBk6VG1sjs/3IVjzLuv+AbXxKNhkcbf8F6tPYaPzm6H40qhpLdhqCOiSDr/AnU\nl0KxqFaP0l+tBFnDo62/Id+/S9aZw1i8WZvSM1eALJN1OZSs8yfNqxet7/t75u+MWTtd5/sOEHf9\nDj3GvkP0hZuEB4bSb+pgSlmXYoSe7/tZ5/u2zFvLhPVfgQQxFyMJ2Wj6fWoKy8SvFnD67HnS0zNp\n23MQn304mD65NnIsSjRqDbtnrmbI2skolArCNgeTdD2WNmP7EHshimuBYTQe2oGKzWugzlLzKOM+\n28b/CkDjIR1wKO+K3xe98PtCG9uvHbyA+2aI7fWR1RoOzVhDz3Xae+/ypmBSI2JpMq4PCReiiAoI\no9awDpRrUR3NU63m/bp773HGA8J+38O7u2YjyzLRB8OJPpB3Dx5TolZr+GHGTyxavwCFQsGeTXuJ\njojh/QlDuRYewbGA47xZ+03m/j4LGzsbmrZvyrBxQ3m/7XA0Gg3L56xg6aZFSJJExPkIdv1tnr0y\n9NGoNfw58ze+XPsVCqWSQ5sDuXP9Nv3GDSDy/A3OBJ5m0JfDKGVdirHLJgGQHJfEouHzadq1OVUb\nVaNM2TL49dVu9Llswo/EXI4yiTa1Ws3oMdPx3/03SoWC1Ws2cflyBLO+mkDomXB27Qrgz1UbWbP6\nR65ePkJaWjrvDfoM0G74/c8/O7kQfpAstZovRk9Do9Hg7u7Kn398j1Kp0I7D/tnJbv8ce/FO/+4s\nXPSLSbR/OXEuG7b+rhujbsseo547e5H9z8aoK77leNhe3RhVa9syMjJZ8ctq9h7YgizLBAWEELg/\nGHcPV8ZOHEHEtZsEhGg39/xz5d/8vc40Yz61Ws2k8V+z9d9VKJVK1q/bwtUr15k6fTTnwi6yxz+I\ndWs28+vvSzgTHkRaWjofDhvz3M90c3Vm2cpF2dd7+zZ/9u09+NxzCqt54vhZbP9vDUqlgnVrtZqn\nTR9DWNgF9vgHsXbNJlb+vpRz5w+QlpbB+0O/AKBpswaMHTeCp1lZaDQaxo2Zmb2JfZGg1pA4Zzle\nf8wFhZKMrft5cuMWjqMG8+hiBPcPnsR+cA9sWjdBVqvRZNwlfuqSotMnEJgBqbD1xSRJmgXck2V5\nse71JGCI7vAKWZZ/0r2/CagG7AZ+B/7RZYkjaQtjXQSGo62NXkOW5TG6Y3d0r9MlSRoO1AAmAwfQ\nTt4rgDXANuAQUE7W+xGSJJ0H3gfGAolo68G7AGNkWd6ja+OBtpTMTFmW5+md2w3tJL0NkAzE6Nrc\n0E3wj5Rl+ZyubSVgPdqs/u3AZFmWyz7v2k31ea84ywQVmseYf3LQ1DR7rCxuCYXm8wfmz3Y2JTaW\n1i9u9IrxhpXrixu9YvgqjT6rfGVRF2sVtP+NJk9LFreEQtGtlvk36jM1nc69fn7k8A/ti1tCoaj0\nyaYXN3rFSH30v2+8Vlxc8K3y4kavENvum38TblMzolO+Wx69sozb93r56uWhC4tbwv/EnAYziltC\ngXHUFGbB96vBdk3xroj+X3BV2ry40SvEVtXpFzd6xXCyti1uCYXmcdbT4pZQKNTy6xcnn/Y2/2bn\npubNq3teP8P8CjCv/MDXb4Cfi2kx64vl/77QGe2yLM/K9Xoh2mzw3O3eyfVWHb1jMtoJdgCDLaRl\nWfbS+7f+9sLNjcjxNvK9z4pMD8p9TK9NHNoM/Nzv7ySnPnzuYy1yvb4BNNZ7y/TrmgQCgUAgEAgE\nAoFAIBAIBAKBQPDK87I12gUCgUAgEAgEAoFAIBAIBAKBQCD4/5qXrdEuEAgEAoFAIBAIBAKBQCAQ\nCASC/wO8foWNXh1ERrtAIBAIBAKBQCAQCAQCgUAgEAgEL4GYaBcIBAKBQCAQCAQCgUAgEAgEAoHg\nJRAT7QKBQCAQCAQCgUAgEAgEAoFAIBC8BKJGu0AgEAgEAoFAIBAIBAKBQCAQCJCLW8BrjMhoFwgE\nAoFAIBAIBAKBQCAQCAQCgeAlEBPtAoFAIBAIBAKBQCAQCAQCgUAgELwEYqJdIBAIBAKBQCAQCAQC\ngUAgEAgEgpdA1GgXCAQCgUAgEAgEAoFAIBAIBAIBmuIW8BojMtoFAoFAIBAIBAKBQCAQCAQCgUAg\neAlERnsRckO+X9wSCkUlqXRxSyg09xRScUsoNJlPHha3hEJRwca9uCUUmltPUotbQqFxKmVV3BIK\nRUVeL70AyuIWUEiGXHr9bPKl9EvFLaHQBI+6XNwSCoVjSdvillBo0h+/XvEQwKz7r9f9d1/OKG4J\nheb3bSnFLaHQ2Fi8fn35dWRG6JzillAopjeYVtwSCoWVbFncEgpNgvpecUsoFKUsShS3hEJTt4xP\ncUsoNGGZUcUtoVDce/qouCUUmti0MsUtodC8WdwCBP/fISbaBQKBQCAQCAQCgeD/OHMazChuCYXm\ndZtkFwgEAoFA8P83YqJdIBAIBAKBQCAQCAQCgUAgEAgEaF6/YhGvDKJGu0AgEAgEAoFAIBAIBAKB\nQCAQCAQvgZhoFwgEAoFAIBAIBAKBQCAQCAQCgeAlEBPtAoFAIBAIBAKBQCAQCAQCgUAgELwEoka7\nQCAQCAQCgUAgEAgEAoFAIBAI0CAXt4TjKMLqAAAgAElEQVTXFpHRLhAIBAKBQCAQCAQCgUAgEAgE\nAsFLICbaBQKBQCAQCAQCgUAgEAgEAoFAIHgJxES7QCAQCAQCgUAgEAgEAoFAIBAIBC+BqNEuEAgE\nAoFAIBAIBAKBQCAQCAQCUaH9JRAZ7QKBQCAQCAQCgUAgEAgEAoFAIBC8BGKiXSAQCAQCgUAgEAgE\nAoFAIBAIBIKXQJSOEQgEAoFAIBAIBAKBQCAQCAQCAZriFvAaIzLaX3Hq+NXlhwPL+Cn4V3p+2ifP\n8a7Du/Nd4M8s3vsDM/+ejZOnMwA+1XyZt/1blgb8xOK9P9Csa4si0/yGXy3GBS1mwqGl+H3aLc/x\nRgPbMnrvAkb5z+eTLV/hUskTAOuyNgzfMI1Zl/6k+9fDikxvbjxb1aJXyCJ6H1lCzc/z6n9G+S4N\nGRb7F461fItQnZb27f0IDz/AxYvBTJjwaZ7jJUqUYN26n7l4MZiQkH8pV84LgDZtWnD06C5On97H\n0aO78PNrVmSam7RqyMaQNWw58heDPx+Q53idxrVYvXcFh2MCad2lpcGxI7cCWbP/N9bs/42Fq+YW\nid4WrZuw6+hm9pz4h+GjhuQ5Xr9JHbYErCE89igdurYxOLZiw/ccjwjkl7+WFInWZ9T0q8vCAz+x\nOPgXun7aK8/xTsO7sSDwB+btXcqUv2fhqLMXzyhlY8UPJ39jyOzhRSWZSn61GHlgEV8EL6GFEXvR\nYGBbPt23gBH+8/ngn5k4V9baiwotavDxrrl8um8BH++ai2+zakWm+RkerWrRI2QRPY8socZzbEW5\nLg0ZUky2AqC+X31WHlzJ7yG/0++zfnmO12hUgx93/8jOyJ00f7u5wbH3p77PsoBlLAtYRstuLfOc\naw5eR/vm1Lo2bx1dylsnvsd3VPd827l2bUynhI3Y1q4AgJW3M+2j19IsaAHNghZQbeGHRSWZ5q2b\nsOPIRnYd38IHIwfnOV6/SR027V9N2J3DtO/aOvv9N6tXZt2ulWwLXs8/B9bRsUdbs2ls396Ps+eC\nOH/hEOPHG+8La9b+zPkLhzgUbNgXjhzdyalTezlydCd+fk2zz7G0tOSnn+dzLvwAYWeD6NGjk9n0\n1/Kry6IDP7Ek+Be6GbHJnYd349vAH5i/dylTjdhkKxsrfixim1zXrx4/H1zOspAV9P6sb57j3Yf3\n4MegX/hu3498vWEuznqaZ6ydxV8XNjBt1cwi06vPW22asvf4VgJObefjL4bmOd6gaV22B/3FZdUJ\nOnYzX799Ec1aN2bb4b/579hGho0clOd4vSa1Wb//D07dPkTbLq2y33f3cmX9vj/YELCKLYfW0WdI\njyLTXMmvFl8ELWL0oSW8lY+v/nzvAj71n8+HW2birIvtK7aowYidc/l87wJG7JyLb9Oi99XGmD5/\nKS27vEvPQSOKW4pR3vCrzYSgJUw89B2tPs3rUxoPbMeYvd8y2v8bRuiNpYqS+q3q89uh3/jj8B/G\nY4vGNfjJ/yd2Re2ixduG49APpn7A8sDlLA9cXmSxBUCjVg1ZG7yK9UfW8N7n7+Y5XqtxTVbuWU5Q\n9D78urxlcMzFw4VF6xew5uAfrD7wB25ermbT2a59S8LOBRF+4SDjxufto1rf9xPhFw5yMHg75cpp\n//9bt2nB4aM7OHlqD4eP7jDwff36dePkqT2cOLmH7f+txtHR3izaX5d+0bptC46G7uHE2X2MGvtR\nnuMlSliyctVSTpzdx56gTXjrrrF3OU+i488RdHg7QYe3s/C7WdnnWFpasviH2Rw7s5cjp/3p0r2D\nSTWbIybas3cjZ88FcfyEP8dP+OPs7GhSzc9waF2bxke/p8mJHyk/Kn/f5dy1MW0SNlNGFye79mlB\nw6CF2X+tVRuxqV7eLBoFAlPyymS0S5KkBi7ovdUTcAKGyLL8hYm+IxpoIMtysik+z9woFAo+nPMJ\ncwZ+RWp8Ct/sWExo4CnuXL+d3SbqUhSTu47jyaMndBjUicFTh/HdyEU8fviYn8Z+T3y0CnsXB77d\nvYRzIWd5kHnfrJolhUT32e/zx6BvyIxP4fMdc7kSEEbijdjsNuH/HePU+iAAqrarR5cZg1g19Fue\nPn5KwJJ/cH3TC7c3vM2q83n6G88byv4BC3igSqWr/2xu7T9DxvU4g3YWpUtR9YOOJIXdKHKNCoWC\n77+fQ5cuA4mNjefIkR3s2hXI1avXs9sMG/YOaWkZ1KjhR79+3Zg3bwqDB48kJSWNvn0/QKVKpFq1\nN9i5cx0VKzYuEs3j541m9ICJJKqS+NP/Vw7vP0b09ZjsNvGxCcwZ+y0DR7yT5/zHj54wtEPeIMic\neqctmMhH/UeREJfIpn2rObjvMDcjorLbqGITmDZ6DsM+HZjn/D+X/YWVVSn6Dck7sWIuJIWCoXM+\n4tuBX5Man8LsHQsJCzxN3PU72W1iLkUxs+tEnjx6QttBHXl36hB+GZnzMKDv+AFcPXmpCDVLvD1n\nGOsGfkNmfCof7ZjDtcAwkq7n2IsL/x0jVGcv3mxXj47TB/LX0IU8SLvLhg8WczcxHZc3vBi0bjJL\nG48qUu2N5w0lQGcr3vafze1XzFaAti9/Nvczpg2cRrIqme93fs+JgBPc1vMjiXGJLB2/lD6fGD7M\nbdimIZVqVGJkp5FYlrBk4ZaFnD54mof3HppV7+tm31BIVFvwAaf7z+NRXApN980ncd8Z7kfEGjRT\nli5F+eGdSD9z3eD9BzEJHGs7xfw69VAoFHz5zXg+7j+aBFUiG/b+yaH9h4mMiM5uo4qNZ/roOQz7\nzNDGPXr4iGmjZnMr6g7Ork5s3L+KYwdPcjfznsk1Lv1uNt26DiI2Np7Dh3ewe3cAV6/m3EtDh/Un\nPT2DWjVb0bdvN+bMncLQIc/6wofE6/rCfzvWUrlSEwAmTR5JUlIKdWq3QZIkHBzKmlT3M57Z5AV6\nNvlMLpscfSmKGXo2ecDUIfxcjDZZoVDw8dwRzBo4gxRVCgt3LuVUwEmDuDPyUiQTuozjyaPHdBzU\nmSFfvs+SzxcC8O+KbZS0KknHgZ2LTLO+9q8WTOb9fp8TH5fA1v1rCdobYui378QzZdQsPvws74Ol\notQ5ef44PntnLAmqRP7a8zvB+48QpX/v3Ulg1uj5DP7UMCkhKSGFYd1G8PTJU6ysrdhyaC3B+46Q\nnJBiVs2SQqLr7GGsGaT11Z/smMPVgDCSbuTvqzvNGMi6oQu5n3aX9R/m+OohayezuEnR+er86Pl2\ne97r050v5ywubil5kBQSPWe/z++D5pMRn8LIHfO4HHDGYCx17r+jnFwfCEDVdvXpOmMwfw5dUGQa\nFQoFn8/9nC/f+5JkVTI/7PqBkwEnuXX9VnabxNhEloxbYjS2qFijIp93/FwbW/yzkNCDoTy498Ds\nmkfPHcWE9yaTpEri192/cHT/MWJyaV4wbiHvfNI/z/lf/jCZdT+u58zhMKysS6HRmGebwGe+r3vX\nwcTGxhNy+D/8dwca9X21a7amb9+uOt83ipSUVPr1HZ7t+/7dsYY3KjVFqVSycNFMGtTvQEpKGnPm\nTuGTEUOYP+8Hk2t/HfqFQqFgwZKZ9O/5AXGxCew7uIV9/geIuHYzu817Q/qSnp5Jk7od6dnnbWZ8\nPZ6P3x8HQEzULdq+lXeMN2bCCJKTUmhWvxOSJGFvb2dSzeaIiQA++GAMZ8MuGPtaE4mXeHPBh5zt\nP5fHcSk02PcNSftCeWAkTvYe3pmMMxHZ7yVsPULC1iMAlK7qTa01k7h3KQaB4FXnVcpofyjLch29\nv2hZlkONTbJLkvTKPCAwJ5XqVCY+Op7E2wlkPc3i6M7DNGjfyKDNpeMXePLoCQARZ6/h4K59CqmK\niiM+WgVAWmIqGckZ2DrYml2zd51KpMQkkHY7EfVTNeE7j1O1Q32DNo/1JmlKWJdElrWBytOHj4kJ\nvUbW46dm15kfTnUrcjc6gXu3ktA8VRP13wnKdayfp129SX25uHwX6kdFr7VhwzrcvBlNdPRtnj59\nypYtO+natb1Bm65d27N+/VYAtm3zp1UrbZZqePglVKpEAC5fjqBkyZKUKFHC7Jqr1a3Cneg44m6p\nyHqaReB/B2jZ0TBzNv5OAjevRKLRFP8ipZr1qnE76g53YuJ4+jQL/38DaN3JMLMi7raKiMs3kI3o\nPXk4lPtmHjDkpmKdSiREq0i6nYD6aRYndh6hfi57ceX4xWx7ceNsRLa9APCpUQE7p7JcDAkvMs2e\ndSqSGp1A2u0k1E/VXNx5gjfb528vLK1LZu9+Hn8phruJ6QAkRtzBoqQlyhJF5xocc9mK6P9O4G3E\nVtQpRlsB8EadN4iLjiP+VjxZT7MI2RlC0w5NDdok3kkk+mp0nnuvXOVyXDhxAY1aw+OHj4m8HEmD\nVg3Mqvd1tG9l61XiQVQ8D2MSkZ+qif/3GK6d8l6nylP6E/XLTjTF1Bf0qVG3Grei7hB7K46sp1ns\n/TeQ1h1z27h4rl+5madfxETe5laUdrI4KSGZ1OQ07B1NP1ndoEEdIm/GZPeFf/7ZSdeuhtlhXbt0\nYP1f2r6wfbs/rVppVzGEh18iPp++MGRIPxYvWgaALMukpKSZXDuYxibbOpXlQhHa5Mp1KqOKVpFw\nSxt3HtkZQqMOhg+rLh6/wJNHjwFt3Omop/nC0fNmfRD3PGrVq05M9G1ux8Ty9GkWu//dT7vOfgZt\nYm+ruHb5Bhq5+OKMGnWrcic6597b918grToaZnWq7hi/97KeZvH0idZ+lChpiaQommGcV52KpMbk\n+OoLO09Q5QWx/TNnXdy+Oj8a1KmJnW2Z4pZhFO1YKp5UvbFUtQ6GPiXv9TbPpG9+5I4tgncE06RD\nE4M2z2ILOZe2cpXLceFkTmwRdTmK+q3yxk+mpkqdN4mNjkOlG4sc+O8QzTvkHYtEXonKE9uXr1wO\npVLJmcNhADx88IjHOjtoaho0qJ3H93XJFQd16dJez/ftyfZ958MvG/V9kiQhSRLW1tYA2NraZMdL\npuR16Rf16tciKvIWMdF3ePr0Kf9u86dTF8NVTp3ebsvmv/8FYOe/+2jh19TYRxkwYFBvfly6EtDG\nF6mp6SbTbK6YqCiw1cXJj3RxcuK/x3Du1DBPuwpT3iHmlx35xsmuvVqQsP2oueUKBCbhVZpoz4Mk\nSa0kSdql+/csSZJWSpK0H1grSZJSkqRFkiSdliTpvCRJn+idEyJJ0nZJki5LkvSrJEl5fqckSf9K\nknRGkqRLkiR9rPd+J0mSwiRJCpckKUj3XmlJkv7UfddZSZJ66N6vLknSKUmSzuk0VDbl73dwcyRF\nlZN8n6pKwdEt/+U8bd9pz9lDZ/K8X6l2ZSxKWJAQE29KeUaxdbUnIy4nsyZTlYqdq0Oedk0Gt2dC\n8Hd0mvIeO2etNbuugmLtZs/9uNTs1/dVqVi7GS6tc6heHmt3B+4EnitqeQB4eLhx544q+3VsrApP\nTzcjbbSZtWq1mszMu3mWCPbq9Tbh4Zd48uSJ2TU7uzmRGJcT0CWqknB2cyrw+SVKluBP/1/5becv\neSbozYGrmwuquITs1wlxibi6OT/njOLH3s2RVFXOvZeqSsHeLe+99wy/d9py/pB2wCBJEu9NH8aG\n+WvMrlMfWzcHMlWG9sLWLe9S1oZD2vNFyFLaTx3Anq/yaqz2diPiL8WgfpJlVr365LYVD/KxFaXd\nHYgtJlsB4OjmSHJcjh9JViXj6FqwZaGRlyNp0LoBJUuVxNbellrNauHkXvD79n/hdbRvJd0ceKjn\n9x7FpVIy171XpoYPpTwcSQoIy3O+VTlnmgV+Q6PtM7FvXMXsegFc3Z1J0LPJCapEXNwLb+Nq1K2G\npaUlt6NjX9y4kHh4uHInNmeFSGysCncP13zb5NcXevbszHldX7Cz0yYczJw5nqPHdrHur19wcTFP\nn/5fbHK4nk0eWAw22SGXvUhRpTzXXrR7pz1hB/PGncWBq7sL8bE5fjs+LhFXd5diVGQcZzdn4mMN\n4yGXQsQXrh4ubApajf+Zbaz5eb3Zs9kByrg65IntbV3z+upGg9szJngpHaYMYPcsI766cyNUReyr\nX0fsXO1J17veGaoU7Ixc76aD2zMp+HvenvIe/xm53ubEyc2JpLik7NfJquTnjlH1iboSRYNWerFF\n01o4e5g/xnZ2dyJJb3I5KT4JZ/eCafau4MW9zHvM/u0rftv7KyOmf4zCTA+6PDzcuBOrHwfF4+GR\nOw5yzW6jVqvJeIHvy8rKYszoGZw8vYcbkSepUqUya1ZvMrn216VfuHm4Eqd3jeNi43FzN4wv3N1d\niNW7xncz72avgCtX3ovAw9vYvnsdjZtqHwbY2mkf3E2eNpqAkK38tuZ7k5ZhMUdM9IwVvy7i+Al/\nJk8xz2qjkm4OPNazaY/jUvLEyTY1fCjp4USKkTj5Ga49moqJ9iJGg/za/xUXr9JEu5VuwvqcJEnb\n82lTH+ghy/J7wIdAhizLDYGGwEeSJD0rgNsIGA/UBCoCvY181geyLNcHGgBfSJLkKEmSM/Ab0EeW\n5drAs8Ji04ADuu9qDSySJKk0MAL4QZblOrrPuZP7SyRJ+liSpFBJkkIj70UX7ooYIffT32e81cuP\nCjUrsWOF4aUr62LPqO/GsmzCj/mea1IkKc9bxr73xLoAFvuNZe+CDbQZ1dP8ugqKEf0G96ck0WjW\nIEJn/11kknJjVGKuayy94P+hatXKzJ07hZEjp5pcnzFepOdF9Gr0Dh+8PYKvPp/LmK9H4lnew5Ty\n8mK0GxSfoS4IRiTnm+DUrFdLfGtWYvcKbaZG2yGdCD8YZjApVFwY6xen1wbwY8txBC7YSMtc9sK5\nsiftprzLzql/FJVEwHifzm0rGhSzrdDK+N/vvbOHz3L6wGkWb1/M5J8nc/XMVTRq82aCvo72zejN\np98ZJImqs4dwbdZfeVo9SkgjuN5IjrWbytWv1lFr+SiUNlZmk6qvKTeFjRGcXByZ/9NMZo6Za5b4\nokB9twB9Yc7cKYwa9SUAFhZKvLw8OH48lObNunLqZBjz539pWuHPpBl7M5/L1LxXSyro2eR2Qzpx\nrhhscmHshV+vVlSsVYl/V2wzt6wCURDb8SrwsvFQQlwi77QdRo+m79C1fyccnMxTZ1mfgl7bU+sC\n+N5vHPsXbMTPiK/uMOVddnxZtL76tcRoH8nb7Pi6ABb6jWHPgr9pO6roShUC+QSdBTs1LCSM0IOh\nLPl3iTa2CLuKOkttUnnGKdh1NYbSQknNRjVZPmclI7p8hns5dzr1N2397WcUxEYUJA6aPXcyX4ya\nBoCFhQXDPxpI86ZdqVShMRcvXmXCxM9MrJzXpl8Ys2l5OkM+92FCfCL1qreh3Vu9+WraApb/vhib\nMqWxUCrx9HLn1Mkw2rfsQ+ipc3w1d5IJNZs+JgL44IPRNGrUifbt+tG8WUPee8/YtNlLYkxXrji5\n8uyh3HhO8qVtvUqoHz7h/tXb+bYRCF4lXqWJdv3SMflFCztkWX62Vq4DMESSpHPAScAReJZRfkqW\n5UhZltXABsDYTqBfSJIUDpwAvHXnNgFCZFmOApBl+Vm6Ygdgiu67DgGlgHLAceBLSZImA+X1tGUj\ny/JKWZYbyLLcoIKNT4EvBkBqfAqOetmDDu6OpCak5mlXs3lteo/sx7fD55GllyViZWPF1FUz2LD4\nL66fjchznjnIjE/FziPn6a2tuwOZifkvyz6/8zjV2pu3FEFheKBKpbRHzhPW0u4OPEjI0W9pU4qy\nVbzo9M80+p74Dud6FWm7alyRbnIYGxuPl5d79mtPT3fi9LKvtW1UeHlpJ6OVSiW2tmWyl695erqx\nadNKhg8fR1TULYqCRFUSLh45mWUu7s6FysJ61jbuloqw4+d4o0Ylk2vUJ0GVaJAl4OrhQmL8q721\nQ2p8ikHZAQd3R9KN2IvqzWvRfWRfvhv+Tba9qFzvTdoN7czSI78yYNpQWvRuRf/JeTdoMzWZ8anY\nuhvai7sJ+S+zvLjjOFX0lk/bujnw7sqxbB/3K2m3TL8E9nncz2UrrPOxFR3/mUZvna1oXcS2ArTZ\nRE4eOX7Eyd2J1MS8/SI/Nv28iVGdRzFt4DQkSSI2yvSZy/q8jvbtsSoVKz2/V8rDgcfxOX3BwqYU\nNlW8aLRtJn6nf8KufiXqrZ2Abe0KyE+yeJqmrW2eeT6Kh9EJlK7onuc7TE1CXCKuejbZ1d2FpELY\nuNI21vzy1xJ++nYl58PMU0M8NjYeL8+ch6qenu7ZS5+fEafXJndf8PB0Y8PGFXyk1xdSUtK4f/8B\nO3bsA7Slh2rXqWEW/cZsctpzbPJSPZtcqd6btB/ame+O/Mp704byVu9WvFMENjkll71wdHc0ai9q\ntahN35H9+ebDuQZxZ3ESH5eIm2eO33bzcCExPuk5ZxQPiapE3DwN46GkhMLHF8kJKURei6Ju49qm\nlGcUY7H9s3Iwxri48zhV2xv66gErxrKtGHz160hGfCpl9a63nbvjc8dS4TuPU72Ix1LJqmSDbGMn\ndydSChHXb/xpIyM7jWTawGkgQVxU3ItPekmSVEk4661ycXZzJjm+YJqTVMncuHQD1S0VarWGI/uO\nUrmGSReyZxMbq8LLUz8OckOlyh0HxWe3USqV2OXyfX9vXMHHw8dn+75atbWbED97vW3rbho3qWdy\n7a9Lv1DFJuChd409PN2Ijze0Taq4BDz1rnEZ2zKkpaXz5MlT0tK01/r8uUtER92mYiVfUlPTeXD/\nAf47AwDY+e9eatY23ebP5oiJnv1OgHv37rN58w7qNzC9T3msSqGknk0r6eHIE704WWlTitJVvKm7\n7Suanv4Z2/qVqbV2UvaGqAAuPZuLbHbBa8WrNNFeEPR38pSAUXqT876yLO/XHcv97NTgtSRJrYB2\nQFNd5vpZtJPnkpFzn31XH73vKifL8hVZlv8GugMPgX2SJLV52R+oz43w67j7uuPi7YKFpQXNu71F\naMApgzY+1X35+JtP+fbDeWSmZGS/b2FpwcSVUwneepAT/sdMKeu53Am/iZOPG/ZezigtldTu1pQr\nAYbLih19cpa/vdmmLsnR5i9pU1CSz0Vi6+uGjbczCkslvj2acHt/zhKmp3cfsrHmp/zTZCz/NBlL\nUthNgt5fSsr5qOd8qmkJDQ2nUiVfypf3xtLSkn79urF7d4BBm927Axk4ULvJTO/ebxMcrO0Ddna2\nbNu2ipkzF3L8eGiRab5y7irevp64e7thYWlBux5tOLy/YP2yjJ0NliUsAbCzt6VWwxpERZh3E5SL\nZ69QroI3nuXcsbS04O2e7Tm4L8Ss3/myRIbfwM3XHWdvF5SWFjTp1oKwgNMGbcpX9+X9b0bw3Yff\nGNiL5aO/Z2yzTxjXYgQb5q3hyLZDbP42b/atqYkLj8TR142y3lp7UaNbE67lshcOPjkTJ5Xb1CFV\nZy9K2Vrz3qoJBC7cxO3QonmQqE/KuUjK6NkKHyO2YnPNT9nWZCzbdLbiYBHbCoCI8Ag8fD1w9XbF\nwtKClt1aciLgRIHOVSgUlCmrXQrrU8UHn6o+hIXkv6TTFLyO9i3j7E2sK7hhVc4ZyVKJW89mJO7L\n6cdZdx9yoNrHBDccRXDDUWScuUHYkMVkhkdi6VgGFNpMH6vyLlhXcONhTEJ+X2UyLp27QnmdjbOw\ntKBTz3Yc2n+4QOdaWFrw/apv2bllDwE7D5hN45kz4VSs5EP58l5YWlrSt6+RvuAfwMBB2r7Qq1eu\nvrB1FV/NXMiJE4Y2xd8/iJYttTVjW7dubrDRrikpqE3+4JsRLDVik8c0+4SxLUbw97w1HN52iE1F\nYJOvh1/H3dcDF529aNGtJadzxZ2+1Svw6TefM//DOWToaS5uLpy9jI+vN17lPLC0tKBLzw4E7X31\n/Palc1fx9vXGw1t773Xs0Y7gfQWbPHBxd6ZkKW1d3TJ2ZajdsBYxN83/QDE2PBIHHzfK6mL7mt2a\ncPU5vvqNNnVI0fPVg3S++taZovfVryN3wm/iWIixVJViGEtFhEfg4ZMTW/h19/ufYwvfqr6cCTF/\nCapr4dfw8vXETTcWadOjFccCCjYWuXruGjZ2Ntg5aDe3rNesDjHXzTMWOXPmfB7f57870KCNv3+g\nnu/rTHDwcQDs7MqwdeufzMrl++Li4qlStTJOTtoEkTZtW3Dt6k1MzevSL86GXaBCxfKUK++JpaUl\nPXu/zT5/w3hmn/8B+r+nXZnTrWdHjoRof4ejo3122aDyPl5UqFiemGhtlvX+vQdp/pZ2L5a3/Joa\nbK76spgjJlIqldmlZSwsLOjUuQ2XL5veTt89exPrCu6U0sXJLj2bkbwvJ05X333IkWrDOd5wJMcb\njiTzzHXOD1nI3fBIbQNJwqVbExL+FRPtgteH4t+N5n9nH/CpJEkHZFl+KknSG8CzVLtGujIyMcA7\nwMpc59oBabIsP5AkqQraTHbQZqj/IkmSryzLUZIkOeiy2vcBoyRJGiXLsixJUl1Zls9KklQBiJRl\n+Ufdv2sBJht1atQa/pi5kmlrZ6FQKji4OYg712/zzrj3uHn+BqGBpxj85fuUsrZi/DLt0qTkuGS+\nHT6Ppl2bU7VRdcqULUPrvtr5/18m/Ej0ZfNO8mjUGnbMXM0Ha6cgKRWEbj5E4vVY2o3tS+yFSK4E\nhtF0aAcqNa+BOiuLhxn32TJ+efb5k478QEkbK5SWFlTrUJ8/By8g8YZ5Myj1kdUaTkxfQ/u/JyEp\nFNzYFEx6RCx1JvQhJTyK28+pG1ZUqNVqxo6dyc6da1EqlaxZs5krV64zY8Y4wsLOs3t3IKtXb+LP\nP7/j4sVg0tLSGTx4JAAjRgylYkUfpkwZxRRdHbZu3QaTlGTe5elqtYYl03/k+78XolAo2LVpD1ER\n0Xw04X2uhF/jSMAxqtZ+kwV/zKGMnQ0t2jdl+Pj3GdjmfXwql2fygnFoZBmFJLHu5w1Emym4zdGr\nZt7Uxazc+CMKpYLtG3Zy81oUI1ZiBisAACAASURBVCd9zKXwKxzcd5gadaryw6qF2JYtQ6sOb/H5\nxI/o4TcAgLX/rcC3UnmsS1sRdHYnM8fO5eihk2bVrFFrWDvzdyaunYlCqSBkcxCx12/Te9y7RJ2/\nydnA07z75ZD/x959h0VxvW0c/84uINgbKthr7GDvYjfGEo0xmtijSTQx9h5b7N3YNdHYu7GLBSzY\nu6CxYcNCr2JDYHfeP3YpC5hI3AX9vc/nurgSds7AzTr7zJmzZ85im9GWn5cMBSDUL4R5vadZNNe/\nZXYdt5qua0egaDVc3epB8F1fGg5uj9+1h9xxv0L17s0oVrc8+hgdryNfsnPwMgCqd29GziJ5cfm5\nHS7GW6bXdZ3Oy9DINMmu6vRcGLOGJolqxTNvX5yMteLpB1ArwPAcLx27lMnrJqPRaji85TCPvR/T\nZXAX7l6/y3m385SsWJKxf4wlc7bM1GhSgy6Du9C3SV+01lpm/TULgFfPXzF7wGyLLx3zMdY3Vafn\n5qhVVN08GkWr4emmY7y485QSwzvwzOsBwYfefqGYs2YZSgzvgKrTo+r03Bi+gpiIl29tby46nY6p\no+ewdNNvaLUadm3ax/07D/lx+Hfc9LzF8cOnKOdcht/+nE7W7FlwaVqXvsN684VLZ5q3aUzlms5k\ny5GVNh0/A2DsgMncuWHeAWudTseQwePYvcdwLKxdazgWxowdxJUr13Hd786a1VtZsXIu164fJzw8\ngu7dDP/uP/TpRrHihRk5qj8jR/UHoI3xWBg7ZjorVs5l5sxxhISE8cMPw8yaO45ep2fNuBUMN9Zk\nD2NNbm+syVfcL/K1sSb3T1ST56ZzTf5j7DLGr/sVjVbDkS3uPPF+zNeDO3Pv+l0uul2g+y89sc1o\ny7ClIwEI9gtmWq/JAEzZPp38xQtgm8mWP86vYvGwBXieuJom2XU6HRNHzWLl1oVoNVq2b9rDvTsP\n6D/iB/72vMXRQyeo4FyWxWtmkTVbVho2q0f/4d/Tsl7HNMmXOOeM0XNZvGkuGq2GPZv388D7IX2G\n9eKm121OHD5NWafSzPlzKlmzZ6F+0zr0GdaLDg26UrRkYQaP74eqGu7EX7dsE/duP7B4Zr1Oz/5x\nq+m2dgQarYYrxnN1o0Ht8b1uOFfX6N6M4nXKo4vVEfXsJTuGGM7VNbo1I2fhvLj0b4dLf8O5em0a\nnqvfZtj46Vy8eo2IiEgat+3Cj7260r5183TNFEev07N73Gp6rR2FRqvh4tbjBN59StNBX/L0+kNu\nuV+mdvdmlKxTIf5aamuia6m0yrh07FImr5+MVquN71t0HdIV72venHc7TymnUsn6Fn2a9EFrrWX2\nX7MBePXiFbP6z7J43wIM1yLzxy5k1obpaDQaDmw5iI/3I3oO7c4dL2/OuJ3lE6dPmLxiApmzZaZW\n01r0GNydno17o9frWTppOXO3zEJRFLyvebNvo6uFcuoYMng8u/asRavVsG7tthTOfVtYsXIeXteP\nER7+jB7x577uFCtemBGjfmbEKMNjn7fuRoB/ENOmzufQ4S3ExMTy+Ikvfb4favbsH8txodPpGDV0\nEpt3rESr1bBp/V/cuX2P4aN/xuvq3xw6cIyN67az6PeZnLt6iIjwZ/zw7WAAatapxvDRP6OL1aHT\n6xg+aAIR4YY3nSeNn8Oi5TOYNG00oaFhDPjRfEvTWaJP9PLlK3bvWYu1lRUarZbjx06z6s9NZssc\nR9Xp8R71J86bf0HRavDbdIyXd55SdPhXPPe6T8g/9JMBstcqwxv/UKIeyR1Rae3DW4Dv46F8KOsX\nKoryQlXVzEkeawAMVVW1laIoE4AXqqrONm7TAJOB1hhmnAcDbYFKwDjj9xWAE8CPqqrqFUXxwbCW\n+nNgF5AfuAPYAxNUVT2uKEoLYCqG2f5Bqqo2VRTFDvgNqG38XT7GTKOALkAMEAB8k2i5mWQ6FP78\nw3iy31EJJVN6R0i1T2I+tps0oG/Yu80i/FA45yz2740+MJGxr9I7QqpVts2f3hFSpThpsK60mRWL\nTXEV5Q/WZu27L/nyoTgWbJllRSxpZ9Za6R0hVYYrln3j0RLuR/r/e6MPTDt7899mb0kv1Zj0jpBq\nN15/fMdFZivb9I6QKq0yFE7vCKk29tKk9I6QamOq/pLeEVLlmu7tSwF9qF5/ZDXuUpj5Z5BbWr1c\nafMB7eZ0JTJt7xx9Xy9iotI7QqrtzVI9vSOkWqPArR/XRd8HYniRrz+q8cuUzPTZlC7/9h/MjPak\ng+zGx45jWBMdVVUnJNmmB0Ybv+IZPyjilaqqyaaoqKpaJNG3Ld6S4wBwIMljr4EfUmg7DUi/qUdC\nCCGEEEIIIYQQQggh0t3HN/1XCCGEEEIIIYQQQgghhPiAfDAz2s0l8Sx4IYQQQgghhBBCCCGEEO/G\n8p+e8b9LZrQLIYQQQgghhBBCCCGEEO9BBtqFEEIIIYQQQgghhBBCiPcgA+1CCCGEEEIIIYQQQggh\nxHv4n1ujXQghhBBCCCGEEEIIIUTq6VHTO8JHS2a0CyGEEEIIIYQQQgghhBDvQQbahRBCCCGEEEII\nIYQQQoj3IEvHCCGEEEIIIYQQQgghhJCFY96DzGgXQgghhBBCCCGEEEIIId6DDLQLIYQQQgghhBBC\nCCGEEO9Blo5JQwUUu/SOkCrBRKd3hFQbWDIkvSOkWv37ZdM7QqrMt1PSO0KqXX/umN4RUi1A/3E9\nz59Ex6Z3hP95sRp9ekdINcdMudI7QqplVHTpHSFVtB/hnAm9+vHdjPpt1MfVZV5i+/HV5Fj143rt\nATx+GZTeEVIll3WR9I7w/8LkS1PSO0KqdakyOL0jpIqt+nHVZKccRdM7QqpdinyQ3hFSLVr3cZ37\nrDTa9I6QapXqf1znPSHSw8d1hhJCCCGEEEII8f/CmKq/pHeEVPkYB9mFEEKIpD6+KV4fjo9vGpQQ\nQgghhBBCCCGEEEII8QGRgXYhhBBCCCGEEEIIIYQQ4j3IQLsQQgghhBBCCCGEEEII8R5kjXYhhBBC\nCCGEEEIIIYQQqKjpHeGjJTPahRBCCCGEEEIIIYQQQoj3IAPtQgghhBBCCCGEEEIIIcR7kIF2IYQQ\nQgghhBBCCCGEEOI9yBrtQgghhBBCCCGEEEIIIdCnd4CPmMxoF0IIIYQQQgghhBBCCCHegwy0CyGE\nEEIIIYQQQgghhBDvQQbahRBCCCGEEEIIIYQQQoj3IGu0CyGEEEIIIYQQQgghhECPmt4RPloy0P6B\nK+3iRLtx3VG0Gs5vOcqRpXtMtrv0+oyanRqhj9XxIuw5m4cvI9w3BIBWI7+hbMNKABxeuAPPfWfT\nJHM5F2e+HtcTjVbDyS1HOLB0l8n2pr1aUa9TY/Sxep6HRbJq+GLCjJl/v7+Fp3ceAxDmG8Ki72ak\nSeY4NtWrk6VfP9Bqeb1/P682bkzWJkODBmTu0QNUlZj794mcPDlNMwJUbVCFPhP6oNVqOLDpIFuX\nbDPZXr5GefqM/4FiZYoy9afpnHI9Fb+t1+hvqdGoOopG4crJqywdvyxNMmeqV4U8v/yAotUQse0Q\nYb+bZs7Wrgn2I3oRG2g4FsLX7+PZtkPx2zWZ7Ch6cDkv3M4SOHGpxfPmbViRShO7omg1PNh4nDuL\n9qbYLn/L6tReMQD3T8cQ7vWQPPXLU/GXTmisrdDHxOI1cSPBp29aPC9AoQYVqT/BkPnmpuNcXmKa\nuXyXRlTo3hRVpyfmZRRHR64k/K4fALlKF6Th9G+xyWyHqqpsbTUO3ZsYi2fO1dCJ0pMNNe7phqP4\nLNyTYru8rWrgtHIQ55qNJtLrAQCZyxai7KzeWBkzn2/+C3oLZ/6veW0L2lPn5Bxe3jc8388u3+XW\n8JUWzRqnaoMq9J3QF41Ww8FNB9myZKvJ9go1ytNnfB9jvZjGyUT1ovfoXlRvVB2NsV4sGW+Z1179\nRrUZO3UoWo2WLet3snzBapPtNjbWzF4yifIVyxAeHkH/3iPxfeKPtbUVk+eMoYJzGfR6lUm/zOL8\n6csm+y5fP49ChfPTot5XFskOkKOhM8Un9UTRagjYcIQni3al2C53q5qUXTGEK81H8MLrAYqVllJz\n+5C5QjEUrYbAbR48WZjyvuZWu2ENhk0aiEarYdeGvaxatN5ke+WaTgydOICSZYszqs943PcdB8Ch\nQF5mr5yKVqvFytqKzSu3s32tZTI3berC7Nnj0Wq1rF69mdmzTY8/GxsbVq6cS6VKFQgLC6dLl348\nfvyURo3qMmnSSGxsrImOjmH06Kl4eJwBYMKEYXTu/AXZs2fD3r6sRXLHydnQiZKTDceF/4YjPFq4\nO8V29q1qUGHlEC42G8lzrwfkbV+XQj+2id+euWwhLjYZwYsbjyyaF8DZpTI9x/dGo9VyZPNhdi39\ny2R7q96f07hTU/SxeiLDnrF42AJCfIMpUrYo303pS8bMGdHr9Py1aCtn9p16y295P/Ub1Wbc1GFo\nNBq2rt/FsgWrTLYnrhcR4c/4ufcIfJ/4Y2VlxbTfxlG+Ymm0Vlp2btnP0vl/UrREYRb+kdDfLFgk\nP79NX8qq5cn7gP9Voyb1mDrjFzRaLevXbGPBvN+TZV6yfBYVK5UjPCyC3j0G8uSxLwUL5efMxQPc\nu/sQgMsXPRk6aDwAo8cOouPXbcmWPStFHCuZLWtKCrtUxMXYv7ix+TiXkvQvKnRpRMVuxv7FqyiO\njFxJmLF/kbt0QRpN+xabLHaoepXNrdOmfxGnlIsTbcZ1Q9FquLjlGMeTXEvV6NyEWl2bour1vHkZ\nxY5RKwi655tm+d7FmKlzOXH6AjlzZGfX+rTpt78LJ5dK9BjfG41Ww9HNbuxeusNke8vebWjUqSm6\nWB2RYZEsG7aQEN9gcue3Z8jykWg0GrTWWg6u3o/7hkNv+S3mU8mlMr0mfIdGq8F9sxs7lmw32d6m\n9+c0+bpZfN5FQ+cT7BsMwNi1E/ik0ifcunSLKT0nWjxrnBoNqjFwYj80Gg17N7myfvEmk+1ONSoy\n4NefKF6mGON/nMTx/Sfit5147MaD24baEegbxIieYyySsVHjekyZ8QtarYb1a7exYN4fJtttbKxZ\nvHwmTs7lCAuL4Lueg+Lr2+kLrtw31rdLl7wYNmg8dna2rFwznyJFC6HT6Th88BiTJswxa+bGTeoz\nY+ZYtFota9dsYd7c5Uky27D8j9k4O5cnLCycnt378/ixL5WrVGT+wikAKIrC9KkL2Lf3MAA//tST\nbj2+QlXh5o07/NhnOG/eRH+wmUuULMqqNQvi9y9SpCBTJ//G0iWrzZY5jpVzdTL27AcaLW+O7OfN\nruTnV+taDbD7qgeoKrpH93k5P9EYi11Gsv22hugLp3i9cr7Z8wlhbmk60K4oSl5gHlATCAeigZmq\nqu5MyxyJ8rQAJgGZAAXYp6rq0PTIkhJFo9B+4rcs6zKFiIBQBu2Zyt9ulwlM1PnzvenD3NajiYmK\npnaXprQe1Zm1/eZTtmElCpQrwuzPRmBlY02/LeO4ddyTNy9eWzizhs4TezO3y0TCA8IYs2c6nm6X\n8L/3NL7N45sPmdx6BNFR0TTo0owOo7qyvN88AKKjopn42TCLZnwrjYYsAwYQMXQouuBgci5bxpvT\np9E9Sriw1ebPT6bOnQnr1w/1xQuU7NnTIaaGnyb/xKhvRhPiH8LCffM553aex3cfx7cJ9g1izuA5\nfPlDe5N9y1YpQ7mqZenT7EcA5uyYTcWaFbh27rqlQ5N3/I886fkLMQEhFPnrN14cOUf0/ScmzZ67\nnnjrIHrugd14deFvy+aMo1GoPLUHJzpO45V/GE0OTMLv8BWee5teeFllsqVk7+aEXr4X/1h02HNO\ndZtNVGAEWT8pQP1NI9hX+WeLR1Y0Cg0md2fXN9N54R9Gx30TeeB2OX4gHeDOrrP8vf4oAEWbVqbe\nuC7s6ToTRauh2YK+uA1YRsitx9hmz4w+JtbimdEolJn+LZe/mkKUXyg1D00l+NBlXiZ5nrWZbCnU\n+1MiLt9N+Hu1Gios/onrPy3mxc3HWOdIg8zvkRfg9aNAzjUeadmMSWg0GvpN/omR8fViAWfdzpnU\niyDfYGb/Y73oC8DcHXOoWLMi185dM3vGCTNG0P3LHwnwC2Sn23qOHPTgnvfD+DYdOrflWUQkjap/\nTqt2zRgxfgD9e4+kY9cvAPisfkdy5c7Bn1sW0bZJF1TVMAOjWctGvHr5yqx5U/gDKDGtF9e/msQb\n/zAqHZxG6OFLvPJ+atJMm8mW/L1aEHnZO/6x3K1rodhYc7nhEDR2NlQ9MY+gXad58yTYwpE1jJw2\nhL5fDSTQP4gNB1fgcfgUD7x94tv4+wYyfsAUuv34tcm+wYGh9Gjdh5joGOwy2rHdYx0eh04RbHyT\n1JwZf/ttEi1bdsbXN4BTp/awb587t28nvK569OhIePgzypd3oUOH1kyZMpKuXfsRGhrOl19+i79/\nEGXLlmLv3nUUL14DAFdXd5YtW8P168fNmjf5H6DwyfReXP1qMm/8Qql6aBrBhy7xKoV6UbB3C54l\nOi4C/zpF4F+GQepMZQpScc3wNBlk12g09J70AxM7jyMsIJTpe+Zwyf0CT+8mnKsf3njAiFaDiY6K\nplmXFnQd1YN5/Wbx5vUbFg6aR4CPPzny5GTm/rl4nrjKq8iXZs/464yRdPuyLwF+gexy24D7QQ/u\neT+Ib/NV57ZERjw31ovm8fXis8+bYJPBhhb1v8LWzpbDp/9iz44DPLz3iFYNO8X//LPXD3Fo/zGz\nZp4xZzxfft4TP98A3I7/xUHXI3jfuR/fpnO3DkREPKO6c1PatW/J+F+H0bvnQAB8Hj6mYd3Pk/3c\nQwePsvL39Zy/ethsWVMS17/Y2dnQv+i019C/CEvSv7ieuH8xtgu7uxn6F83n9+XQwDTuXyTK3nZi\nT1Z0mcqzgFD67ZnCTbfLJgPpnrtPc36DOwBlmlSh1diu/Nl9epplfBdtP2vKN+3bMHrS7PSOEk/R\naPh20g9M6Tye0IBQpu2ZxSX3C/jeTTj3+dx4wKhWQ4iOiqZpl0/pPKo78/vNJjwonLFfjCA2OpYM\nGW2ZfXgBl90uEB4UbrG8Go2G7yf3YULnsYT6hzJz71wuuJ03qW8PbjxgaMvBREe9oXmXFnQb3ZM5\nP80EYNfyHWSwy0Dzzi0sljGlzEOmDGDg18MI8g9mhetSTh0+g8/dhPNBoG8gUwbN4Os+yScTvImK\npkez7y2ecfqccXRo2xM/30AOH9vOQdejKdS3SKpXakbb9p8x7tehfNdzEGCsb/XaJvu5ixf+yemT\n57G2tmbHntU0blKfI+4nkrX7r5nnzJ1A2zbd8fUN4NiJnbi6HuHO7YTruW7dDTW5klMj2n/Zil8n\njaBn9/7cuulNg3pt0el05M1rz+lz+zngeoQ8eXLTp293qldtTlTUG1avXUD7L1uzccNf/5AkfTPf\nu/uQerVbx//823fPxL9pYFYaDRl7DeDFpKHow4LJMm0ZMZdOo3+acBxr8uXHtl1nno/ph/ryBUpW\n0zEWu07fEnvTy/zZhLCQNFujXVEUBdgFnFBVtZiqqlWATkCBd9xfa+Y85YFFQBdVVcsA5YEH/7yX\nyf4Wf5OikHMJQh4FEPokCF2Mjqt7z1C+WVWTNvfO3iQmyvBO6aOrd8meLycAeUvm5/75W+h1eqJf\nv8H31mPKuDhZOjJFnUsQ9CiAkCdB6GJiubD3NM7Nqpm0uXP2BtHGzPev3iVHvlwWz/UurEuXRufr\ni87fH2JjiTp6lAx16pi0sWvVite7dqG+eAGAGhGR5jk/cS6Fn48fAY8DiI2J5fgeD2o1q2nSJvBp\nEA9v+6BXTW/3UVUVmww2WNlYYW1jjZW1lvAQy/8NthVLEf3Ij5gnARATS+T+E2RuUuud989QrgRW\nubPz6tQVC6ZMkLNScV74BPLycTBqjI4nu8+Rv3mVZO3KjfiSO4v3oUs0WyHi70dEBRqe08g7T9Fk\nsEZjY/n3NPM6FyfCJ5DIx8HoY3R47zlHsWammWMSvdFmlTEDGI+PQvUrEHLrCSG3DIOvUREvUPWW\nv1UsW+USvHoYwOtHQagxOgJ2nSHPp1WTtSsx8iseLt6LPiphBlyuBhV5fvMxL24aMseEvwALZ36f\nvOnlE+dP8PPxj68XHns8qN3M9LUX+DSQh7cfxg9Ox1FVUqgX5r8IdqpcnkcPn/LkkS8xMbHs23mI\nJi0amLRp0qIBOzbvA+DAniPUqmc4r5T4pBhnTl4AIDQknMhnz6ngbJilnDGTHb36dmbxnBVmz5xY\nlkoleP0wgKjHQagxsQTvOk2u5smPi8IjOvFkyW7Tuy5UFW3GDKDVoLG1QR8di+65Zd8QByhfqQxP\nHj7F97EfsTGxHNp1hAbN65m08X8SwN1b99EneV3FxsQSE234G2wyWGPo3plftWrO3L/vg4/PE2Ji\nYti2bS+tWjU1adOqVVM2GC9id+xwpUEDwznby+sG/v5BANy86U2GDBmwsbEB4MKFqwQEBFkkc2JZ\njfUiylgvgnadwf7TasnaFRvZkUeL97y1XuRtV5fAnactHReAEs4lCfDxJ+hJILExsZzee5JqTWuY\ntLlx9np8H+7u1TvkcsgNgP9DPwJ8/AEIDwrjWcgzsubMavaMhnrxxKReNE2hXvy12TDj+sAed2rX\nqw4YalrGjLZotVpsbTMQExPDi+embwTUrl+dRz5P8Xvqb7bMlatW5OGDRzwyHss7/9pPi5ZNTNq0\naNmYzZsM84727DpIvQb/3ke6fNGLwEDLvikHhv7Fs8T9i73J+xfRifoX1nYJ/YvC6dS/iFPQuQSh\njwIIM15Lee09S9kk11KJJyHZJOobfUiqOlcgW9Ys6R3DRAnnkgQa64UuJpYze0+lUC/+TlIvDNd8\nuphYYqMNb7hY21ij0VjmPJJYSeeS+Pv4E/jYUN9O7T1B9Wamef8+e53oqDcAeCfKC3D99DVeW3jC\nWlJlKpXmqY8vfo/9iY2J5cjuo9RrXtukTcDTQO7feoCq16dptjiVq1TE58EjHvk8JSYmhl079tOi\nZWOTNi0+a8SWjYb6tnfXIeq5/HN9e/06itMnzwMQExPDNa+bOOTPa7bMVao68eDBo/j+xY7t+2iZ\npCZ/1rIJGzcY7tDYtfMALsaa/Pp1FDqdDgBb2wwmfWetlRV2doZzjJ2dHQH+gR985jgNGtTm4YPH\nPHnil2zb+9KWKI0+wBd9kGGMJeb0UWyqmo6xZGjSijcHd6G+NI6xRCaMT2iLlUKTLScxXpfMnk0I\nS0nLD0NtBESrqhp/v5uqqo9UVV2oKEoRRVFOKopyxfhVG0BRlAaKohxTFGUjcN342C5FUS4rinJD\nUZT4t2gVRemlKIq3oijHFUX5Q1GURcbH7RVF+UtRlIvGr7hX9XBgiqqqt41ZYlVVXWLcp7WiKOcV\nRbmqKIq7cSY+iqJMUBTld0VRDgNrFUUppyjKBUVRPBVFuaYoSklzPmHZ8+Ykwi80/vtn/mFky5vz\nre1rfNWQW8c9AfC79ZgyDZyxtrUhU44slKxVluwOlh/QzpE3J+F+CbPawv1DyfEPmet91Yjrx6/G\nf2+dwYYxe2YwaufUZAP0lqaxt0cfnHDBog8ORmtvb9JGW7Ag2gIFyLFwITmWLMGmevU0zQiQK19u\ngv0Scob4h5D7Hd+suHXlNl5nr7Hp0gY2Xd7AZY8rPLn35N93fE/WeXMRG5BwXMQGhGCdN3nmLM3q\nUGTPYhwXjMYqn+HiHUUh78jeBM1Im2U2AOzy5eSVb8Jr75V/GHb5cpi0yV6+MBkdc+HvfjXp7vHy\nt6xOxN+P0EdbfvZWpnw5eOEXFv/9C/8wMifJDFChexO6nZpDndGd8Bi3FoDsxfKBqtJm/XA6uk6m\ncp+WFs8LYJsvJ1GJalyUXxgZ8pnWiyzli2DrmIsQN9M3WTIWdwAVKm8eRU23aRT5qfUHnRfArpA9\nNd2nUXXnOLLXKG3xvAC58+UyqRfB/iHkeud6cQvPs15svrSRzZc3csnjskXqRV4He/z9AuK/D/AL\nIq9DHpM2+Rzs8fc1tNHpdDyPfEGOnNm5fcObJp+6oNVqKVDIkfJOZeIvxgaN+pGVS9bz+nWU2TMn\nlsEhJ28SHRdv/MOwSXK+zVS+CBkccxGW5LgI2XcO3as31Lz2BzUuL+Xp0r3ERrywaF6APA72BPol\nDDYH+gdh72D/D3uYyuuYhy1H13Dg8k5WL95g9tnsAI6O+XiaaLDT19ef/PnzpdDGcFGo0+mIjHxO\nrlymda9du8/w8rpBdLT5bt9+FxnyJTku/EKT1YvM5YuQwTE3oSnUizh5P6+VZgPtOfPlIsQ/4d8y\n1D+EnP9QLxp1bMrV45eTPV7CqSRWNlYEPgpIYa/3k88hD/5+CYMX/n6B5E1y7OZ1yJNivTiwx51X\nr6I4d8ONU54H+GPxWp5FRJrs27pdc/buOGjWzA4OefF7mvBc+PkF4OCYN1kbX+PxHncs58xpOJYL\nFS7A0ZO72OO6npq1kr+JZ2mZ8+XgedL+Rd7k/YuK3ZrQ/eQc6o7uhMf4hP6FikrbdcP5ev9kqqRR\n/yJOtrw5klxLhZIthey1ujZluMdvfDbyG3ZPWJOWET9aOfPlJNSkXoSSI9/br/kadmyC5/GEWpfL\nITczD/7GknMr2L1sh0Vnsxvy5iLEzzRvrhSuReI06diUK8eS17e0ZJ8vN0GJztVB/iHY53v3c7VN\nBhtWui7l972LqNe8zr/v8B84OObF1zdRffMNxMHBtL7lc8iLr+8/1bed7N6/jpq1kk9qypotC81a\nNOSkh/mWwHV0TKi3AL6+KdRkx3ymNfnZc3Ia+xdVqjpx7uIBzpx3ZdCAseh0Ovz9A1m4YAV/3zqJ\n9/2zREY+5+hR8y2fZonMiX3xZSu2b095mdT3pclpjz400RhLWDBKLtPjWONQEK1jAbJMWkiWKUuw\ncjaOsSgKdt1+5NU6yy8bzs3BggAAIABJREFUK5JT/we+0ktaLh1TDnjblUQQ0FRV1SjjYPUmIK4n\nWR0or6pq3D3k36qqGqYoih1wUVGUv4AMwFigMvAcOArE3VsyH5inquopRVEKAYeAuBnsb1vs6xRQ\nU1VVVVGU3hgG5YcYt1UB6qqq+lpRlIXAfFVVNyiKYgMkm3VvfDPge4DGOatSIUvxf3qOkuycwmNv\nmWVRpW1dClYsxqKOvwJw5+Q1ClYsxoAdE3kRGonPlbvodWnwTncKM9tSetcUoGbbehSuWJxZHcfF\nPza8dh+eBYWTu2Aehm6agO/txwQ/Nt+7wamWJLui1aItUIDwgQPR2NuTc+FCQnv2jJ/hnhZSmjz4\nrpNvHIs4ULBEQTpX7wrAtI1TKV+jPH+ft/CSLO8Q+vmx80TuO44aE0v2Tp/hMGMIT7qPInvnlrzw\nuGQyUG9pKcdVTRo4/dqFiwOWJ29olLVUfiqO6cSJTmlz+3FKs0pTOi6ur3Hn+hp3SrWtRbX+bXEf\nvByNlRaHaqXY2mocsa+jabt5FEHXfXh6+oaFQ6f0oOnz/MnEbvw9IHnnStFqyVHjE841/wXd6zdU\n3T6GyGsPCTtpwWP5PfK+CQznROV+xIS/IEvFolRaPZTT9Yeis/TsqFTU5KQcizhQqEQhvqneBYDp\nG6dRocZlrpu5XqQ4Izppxrf8Hds27KZ4qaLscl+P71N/rlzwQqfTUaZ8KQoXLciUMXPIX9DBrHmT\n+bdztaJQfGIP7gxYnKxZlkolQKfnvNP3WGXPhNOuSUScuEbUYwvPuH6fEwkQ6BdEx0bdsc+bm7mr\np+G+9xhhZr7b4V/rMG+rewltypQpyeTJI2nVqotZs72TlLIlqRclJ3bn1oAlb/0RWSuXQPc6mpe3\nLf+GOICSwsH8tnpRr10DilcowbiOo0wez54nBz/PG8SiIfPfudakMmQKGZM0ectx4VS5HHqdjlrl\nm5Etexa27PuT0x7nefLIsIyItbUVjT91YdbkheaN/A51OMU2qAQGBOFcrgHhYRE4OZdj7cYl1Knx\nWbKZ+Bb1jv2La2vdubbWnU8+N/Qv3AYvR6PV4li1FJtbG/oXX2wy9C+eWLp/Eecds59d58bZdW44\nt6lN45/bsXWIDOr8m5TqxdtGNeq2c6F4hRJM6PhL/GOh/iEM/3QgOfLkYOgfozjveoZnIc8slPbd\nXodxXNo1oHjFEoz5alSK29NKajKnpH31ToQEhuJYyIEFW+fw4PZDfB+Zd8byf65vqqG+VSrXkPDw\nCCo6l2PthsXUrdkyvr5ptVp+XzmXFcvW8cjnabKfYd7MSdsk3y/u77p8yYua1VpQ6pPiLFs+C7fD\nx7Gzs6VlyyZULN+AZxGRrFm3iK86fs7WLSl/NsuHkDlu/Xhra2s+a9mYXyfMMkvWd5I0vFaLxqEA\nzycMRJPLniwTFxI5uCc29ZsSc+Ucaqjl794SwpzScka7CUVRFiuK4qUoykXAGvhDUZTrwDYg8adT\nXUg0yA7QX1EUL+AcUBAoiWEw3kNV1TBVVWOMPyNOE2CRoiiewB4gq6Io/3bvXQHgkDHPMAxvEsTZ\no6pq3MjIWWC0oigjgMKJHo+nqurvqqpWVVW1aqoG2YGIgDCyOya8057NISfPUni3v1Sd8jTt146V\nvWehSzRz1n3xLmZ/NpJlXaeCohD80Hy3wb5NeEAoORxzx3+fwyEXESlkLlOnAi37tWdR7+nxtw4C\n8X9fyJMg7py7QaFyRS2eOY4+OBhNohnsGnt7dCGmg7u64GDenD4NOh36gABiHz9Gmz9/mmUEwwx2\ne8eEnLkdchMaGPoPeySo3bw2t6/eJupVFFGvorh07BJlKll+Zm1MQEjCDHXAKl9uYoLCTNroI56j\nGtftjNh6ENvyJQCwcy5Dji6tKH50FfYje5G1bWPsh/awaN5X/mFkzJ/w2svokDN+ORgAq8y2ZCtd\nkAY7xvDZhd/IVbkEdVYPIYeT4Xi1c8hJ7T8HcaH/Ml4+svwSBWCcYeaYMJMos0NOXga+feDLe/c5\nihmXw3nhH4bf+dtEhb8gNiqaR8e8sC9fxNKRifIPwzZRjbN1zMmbgITMVpltyVy6ANV2jKPexYVk\nq1IC57VDyepUjDf+oYSduUVM2HP0r6MJcfckawXLZn6fvGp0rGF5G+D5tYe88gkkU3ELDwCTvF7Y\nO+QmLDDsH/ZIUKd5HZN6cfHYRUpboF4E+AXh4JgwUzmfYx4CA4KTtzHOZtZqtWTJmpmI8GfodDqm\njJlD64Zf06frYLJmy4LP/cdUqlaR8k5l8Liyjy37/6RI8cJs2G364YPm8sYvjAyJjosMDjmJDkh4\njrWZ7cj0SUGcdkyg+sXFZK1cknJrRpDZqRh5vqhL2DFP1FgdMSGRRF68TWbn1PUV/osgvyDyOibc\nNZDXIQ/B/+HNzODAEO7feUjlmuZfms7XN4ACBRJeI/nzO+DnF5ikjT8FCjgChuMia9YshIVFGNvn\nY8uW3+ndezAPHz4mrb3xDzU9LhxzEZ2oXmgz25KpdEEq7RhPrYuLyFqlJBXXDieLU7H4Nnna1kmz\n2ewAoQEh5HZIOFfncshNeAr1okIdJ9r368D03pNN+nB2me0YvWocm2dv4O7VOxbJaKgXCbP4HBzz\nEpSsXgSmWC/atG+Bx5EzxMbGEhoSzuXznvFLTQG4NKnLjWu3CQl+txr5rvz8AnAskFDjHB3zEeAf\nlKxNfuPxHncsh4dFEB0dQ7jxmPbyvIHPw8eUKJF2fWMw9BGyJO1f/MPs4zt7zlG8WUL/wjdR/8In\njfoXcZ4lu5bKReQ/ZPfae5ZyTdP+roGPUWhAaPzSUQC5HHK9pV5U5It+XzKz91STehEnPCicp95P\nKF3dsh9OHeofQm5H07xhQcnzVqzrxJf9vmJar8kp5k1LQf7B5El0rs7jkJuQVNxBFmK8PvR77M/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wr4FcOAvwrsV1V1mKIonxtz+mJYoqaaqqoNFEWZALxQVXW2cf9RQBcgBggAvlFV9a33mw4q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\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4+HhCnoYxesgUvemdMn4eO/dtQKlUsHPbfu4E3WfClOH4Xb+Jp/spdmzdy6p1izl/zYOY\n6Fh++XmsRu+61ZvxOLkHWZY54eWDt+cZzMyLMXr8IO7eeYCXjyqX/Mb1O9ixdW+6tY0cNQ23YztQ\nKhRs3uJKYOBdZs0cx5Wrqv5t46ZdbNm8gqBAX6KjY+jRS2VPAwPvsnfvEQL8TxGfkMCIkVM1C8Kl\ndswvMWRwH1q0aEpcXDwx0bH83G9UequZhIQEJo+bw+79G1Aolezcto87QfeZOGUEftdvctz9JNu3\n7mXN+iVcuu5JdHQsA38eDUCjJvWYOGUE8fEJJCYmMG70TGKiYylSpBBbd60lR44cKJUKfH0usHlj\nxkdwJdX3sWR1M3PmOK5q1ffmzSu4ra7vnlr1vWfvEW6kUt+5cuWkZYtmDBkyUed88+dPpkKFssiJ\niTx+EsLQoZMyrD21a1k8ZTlrdi5DoVRyaOdRHt55xOAJ/Qn0C+KMpy9ValZi2caFGOfPR7NWTRg0\nvj+dbXpRurwlY2YNS5oDzj9rd3I/SD8vtpJrnD1pMZv3rEahULB3x2G1fRuktm8+7N5+kN/WzOXk\npUPExMQycsBkQPWycPOe1SQmykSERTJ28HRAla936Nj+3L/7iMMnVSM9t/7tyu5tmU8PkqR5/NhZ\nHDi0BaVSwdZ/9hB0+x5Tp43i2rUA3N1O8M8WV9ZvWIbfjZNER8fS96cRADRqXJfRYwYRFx9PYmIi\nY0bN4OWLaCwtS7B9l2ocjoFSyZ7dh/H28tGL3iTNY8fM5ODhfzSab9++x7Tpo7l2LQC3Y95s2ezK\nhr+X4x9wiujoWPr8OByAXwb9P/buOyqK6///+HNYsMUWk08UrNFoorGDvVAElKaoqCm2JJpoTOw1\nJrHGGDUae++9g1RBrGDDAhZUYsFCMdEAxhYR5vfHLisLqEF2g3x/78c5nCO7d5bXjnfu3Ll7904v\nqlarzKgx3zJqjPaxDh49+fPPu0yb/iN16tQEYOrPc/LUtzdFW2dpWZYVy7XneDMzM7Zt88U/4Nng\nd7eu7Zk2ff7zIuU6v7H3cWLCH/w8ZTa7gzeTmvqUGzfj6Pfl8BfFyEP+dGZ9P5eZG35BY6bBb3Mg\n12Ji6TO8NxejYggLOcwH9d7n5+UTKVGqOC2cmtFnWG+6O3xO5eqVGTl1COmqipmisG7eRmJ/N/1A\nu7aNm8n8jTMx05ixa5M/V2Ou0W/EF0RHXeRgcDi16n3AryumZGrjvqCLXQ/erV6ZoeO+yWjiWLtI\n2rhXMWLcVCJOnyE5+R5tPLvz9Rc96OyR+2+85UVaWhrjRv3Mmq0LMdOYsXWDN79fusKQ0V9zNvI8\ne4IOsHndTmYt/Il9Eb6kJN/jW9016trlm5g+dyK7w3egKLBtg7af/EGt6syYPxmNxgzFzAx/72D2\nBhu7Tf4Rn11r0Gg0rFmzJYf2YgvLls/kzNn9JCUl00vfXvSkarXKjB4zkNFjtOeW9h49+PNP495j\nwFT9ocFDfmDN6rkUKmTB1Ws36NNnKAD9+41g5syJmJub8/jxY/r3H/ncbK/yXqaN/Y05G2ag0Zix\na1MAV2Ni+WrE51yIuqRvK6Ytn0zJ0iVo6dScr4Z/Tjf7XkbLIMR/TcnturaKoqShnWWeIUhV1dGK\nosQCNqqq3lEUxQaYoaqqnaIoxdEuHWONdub3BFVVd2QpPxT4XPd6y1RV/U1RlLbAdCAdSAX6q6p6\nQlGU+sAcoBTaDwp+U1V1qW6t9xXAQ2A32nXcayuKYgcMV1XVPcv7KAIs0uV6CgxVVXWfoii9dbm+\nyVR2NNATeAIEqKr6naIo+3Wve0JRlLeBE6qqVnnRvitSpNLrvGJyNunq63FX+9yoUbpCfkfItXup\nD15e6DUyoajxv9ppals1JvtszGSuPTb+je9MycJMk98Rcu3+07zfrOi/9E/ak/yOkGtpBfA8cufh\nvfyOkCtvFn35t+VeN8mP8nbjw/xQu0yV/I6QK/eePsrvCLn256OXLyn4ukl/rW+Gkt3jpwXvPFLE\nvFB+R8iVem+++/JCr5lH6QWvXqSkPszvCLly8WLuPuh/XdR4v2N+R8iVPx6lvLzQa+TJ09T8jpBr\n9d+ult8Rci0i/uCLVrMQz9GqfJuC1cnJwaG40Hz5v8/1jHZVVXMcTck8yKyq6gnATvfv+0C2j6Oy\nlJ8JzMzy/G60A+ZZt4sEWufw+Ekg8wjgeN3j+4H9OZR/jHaWfNbHVwGrsjw2Fe1s+syP2WX69x2e\ns0a7EEIIIYQQQgghhBBCFATp+br4SsFm7DXahRBCCCGEEEIIIYQQQoj/r8hAuxBCCCGEEEIIIYQQ\nQgiRBzLQLoQQQgghhBBCCCGEEELkQa7XaBdCCCGEEEIIIYQQQgjxf4+s0f7qZEa7EEIIIYQQQggh\nhBBCCJEHMtAuhBBCCCGEEEIIIYQQQuSBDLQLIYQQQgghhBBCCCGEEHkga7QLIYQQQgghhBBCCCGE\nQFVljfZXJTPahRBCCCGEEEIIIYQQQog8kIF2IYQQQgghhBBCCCGEECIPZKBdCCGEEEIIIYQQQggh\nhMgDWaNdCCGEEEIIIYQQQgghBOnIGu2vSpEF7v87b5esUaB29v+KlM7vCLl2L/VBfkfItSfpT/M7\nQq4UxDYjNT0tvyPkWjHzwvkdIVeKWxTN7wi5pqDkd4Rc+fNRcn5HyLWC1r4BVClRNr8j5Ers37fz\nO0KuaZSC94XKR6n/5HeEXClb/M38jpBrDwvYPgbQmBWsulwQj70GJarkd4RcOXHvan5HyLXCGov8\njpBrBa0um6EQc2lnfsf4/0LpSg75HeFfS00reP3kGqUr5HeEXDt3+2jBuuh7TTS2si14Az9ZHI8/\nkC//9zKjXQghhBBCCCGEEMJEarzfMb8j5Ip8MCCEEK+mYH0ULIQQQgghhBBCCCGEEEK8ZmRGuxBC\nCCGEEEIIIYQQQghUWaP9lcmMdiGEEEIIIYQQQgghhBAiD2SgXQghhBBCCCGEEEIIIYTIAxloF0II\nIYQQQgghhBBCCCHyQNZoF0IIIYQQQgghhBBCCIGqyhrtr0pmtAshhBBCCCGEEEIIIYQQeSAD7UII\nIYQQQgghhBBCCCFEHsjSMUIIIYQQQgghhBBCCCFIR5aOeVUyo10IIYQQQgghhBBCCCGEyAMZaBdC\nCCGEEEIIIYQQQggh8kAG2l9DDo6tOHoyiOORIQwc8mW25wsVsmDZyt84HhnC7r1bqVipvMHz5StY\nEht/mgHffq5/rN+A3oQd8+fQUT+WrJhJ4cKFTJa/pX1TAg5vJejYdvp82zPb8zZNG7B9zxrOxh/G\n2d3B4Lklm2Zz7PdQFq6babJ8AHZtWnLwuB9hJwMZMLhPtucLFbJg4fIZhJ0MxDdkIxUqWumfq/lh\nDXbtXs/ewz7sCd+p35cdOruyJ3wnIWE7WLd1MW+WKW3UzA5tWnHkRBDHTwczcEjfHDMvXTmL46eD\nCQrdoq8XFSuV50ZiFPsOebPvkDfTZ03Itu3ajQs5eMTXqHnBNHX5y/49OXTUj7Bj/nz1dS+j5m3j\n2JoTp0I4HbWXIUO/yiFvIVaunsPpqL2E7ttOJV3ehtZ1OXTYl0OHfQk74oe7hzMA71V/V//4ocO+\n3IyPpP/XvY2a2b5NSw5F+HP4VBDfPKcuL1rxK4dPBeG/ZxMVKhnWZd/gDew/sou94d7Z2oVVG+ex\n77CPUfO2cmjG7iPb2XPcmy8H9s4x729Lf2bPcW+2Ba2mfEVLACwszJk6Zxx+Bzaza99GGje3BuCN\nN4qxa98G/c+xi6GMnTzMqJlf9F6Cjmwn5PhOvhyYvS7aNGvAztB1RCccpa1Hm/8kU4Y2jq05fiqY\nk1GhDH5OXV6+ejYno0IJ2bct27FXoYIlNxOj+GbgF/rH5i74mZhrxzh8PMDoeZ2cbDkdGcqZs/sZ\nNqx/jnlXr5nHmbP72X/Am0qVKgDg4NCSsHBfjh8PIizcF1vbZvptLCwsmDtvCpFRezl1OpQOHdoZ\nPXeGlvZN8QvfQuDRbTme96yb1mdryGqi4sKznfcWb/yNIzF7mL/uV5Ply2Ds/Vy8+BscORqg/7l+\n4xTTpv1otLyOTq05eXoPkWf2MmRYvxzzrlw9h8gze9m7f4e+Tba2rkvYET/CjvgRftRf3yaXL2+J\nX8B6Ik4GcywiyGjtsbOzHefOHeRCdBgjRgzIMef69Qu5EB1GeJgvlStX0D83cuQ3XIgO49y5gzg5\n2eof/z3mKKdP7eFERDBHjzw75jp3dicyci//PL6JdcO6Rslv16YFB475EnYigAGDvsj2fKFCFixY\nPoOwEwH4hmzQ94k6ermx+8A2/c+NO2eoVft9ANZtXUTwwe2EHvbm519/xMzMeJccbRxbcezUbk5E\n7mHQ0Jz6FoVYvuo3TkTuIWRv9vatfAVLbiREGrRvkef2EXbUjwPhuwg9sMNoWTMYuw9XtGgRNmxZ\nzOGIQA4d9eOH8cY979m3aUlYRABHXtC3WLxiJkdOBRGwZxMVs/Qt/II3cuCIL/vCfShcuBBFixZh\n3eZFHDruz4EjvowdN9SoebOytrNm6f6lLD+0nC5fd8n2fO0mtZkbMBe/a360dG1p8NznYz5n4Z6F\nLNyzkNYerU2as6DVC1O0FRlWrJ/LnvCdRs0LYOvQgr3HdnEgwo/+gz7P9nyhQhbMWzaNAxF+eAev\n12f29HIlYP8W/c+1PyP1md092xJ0cBsh4TsYM26I0TO3dmhO6DEf9kX40u85mecum8a+CF92Bq+j\nvC6zubk5M+ZPIvDQNkKO7KT/YO22hQoXwjtkPQEHtrA7fAeDR2U///9Xvp8yk9ZuH+HZPfs5/b9k\nin5nhi1blxIRsdvomZ2d7Th39gDR0WGMGP6cvsa6BURHhxF26Flfo0yZ0gTv3sJfdy/x22+TDbaZ\nOGEkVy4f56+7l4yeN7MW9k3xDd9MwNGtfPFtj2zPWzetz5aQ1UTGheHkbq9//P0Pq7POfyneBzaw\nY9862nVwNGlOIYzFpAPtiqKMVRTlvKIoZxRFiVQUpYkp/95zMoxXFCVO9/fPKYrS3kive98Yr5OV\nmZkZv/w6jm6d+9KikSudvNyp8X41gzKf9uxCcnIKjes7sWj+KsZNGGHw/OSfvyM05KD+93KWZen7\nVQ8cbTvRqqk7ZmZmdOzsZor4mJmZ8cMvI/ny40F4tOyGW6e2VKvxrkGZ+LhExgyciP+O4Gzbr5i/\njlEDxpkkW+aMP00fS/cu/bBv2h7Pzq5Uz7KPP+7RmZSUe7S0dmHpwjWMHa+9INBoNMxZPJXRwybi\n0LwDXdx7k5r6FI1Gw8SfR9PF4zOcWnbiQnQMn/X9xKiZp/76Ix959aFFYzc6dn5evbhH4wbOLFqw\nih8nDNc/F3vtBvatPLFv5cmIIYb7183DiQcPHhgta+bMxq7LH9SsTo9eXXG298K2eXuc29pTtVpl\no+X9deZ4vDp9TmObtnTu4sH7H7xnUKZnL23eBvUcWDB/JRMmjQLgQnQMdq08adXcg86en/HbnMlo\nNBou/36NVs09aNXcA9uWHXj06DF+vtnrfV4yT5nxPZ96fYVtEw88vVyz7eOPe3QmJfkezRu2Y8mC\n1Xyvu+jSaDTMW/ILo4ZOwK5Zezq79yI19al+O1cPRx7cf2i0rBl5x08dTZ+PBuLSwgv3jm15L0v7\n4PWpJ/eS7+HY2JOVi9Yz4seBAHTt0REAd9tu9O7yNWMmDkFRFB48eEh7+0/0P/G3Egj232vU3M97\nL+OmjqLvRwNxbdEF947Z27qEW4mM/nY8ftuN39l+WbbpM8fTpdMXNLVpR+cu7tnqco9eXUhJTsG6\nXhsWzl/J+EkjDZ7/6Zex7Ml07AFsXL8DL8/sF3zGyDtz1kQ6evbGuqETXbq054MseXv17kpycgp1\n69gxb+5yJk0eDcDdu0l4eX1B48bt+LLvMJYtn6XfZuSob/jzz7vUr+eAdUNHwsKOGT17Rv6xU0fQ\n75PBtG/1Ea4dnbPXhbjbjB00Kefz3oJ1jPlmvEmyZc1p7P18//4DmjV11f/cvBmHj0+Q0fL+OnMC\nnTt+RiPrtnjl2CZ3JTn5HvXrOjB/3gp9mxwdHYNtyw60bOZOJ8/ezJ6rbZOfpj1l7HdTaGTtTBv7\nzvT9ske213yVnHNm/4SHR3fq1rPno26e1KxZ3aDM5599THJSCjVrtWT2nKVMmTIWgJo1q9Otawfq\n1XfA3f1T5s6ZYjAg7ejUBZtGzjRt5qp/7Pz5i3Tt2pdDh47mKXfm/JOnfU+Prv2xb9aeDp1dqf5+\nVYMyH3XvREryPVrauLJ04Vq+0/WJdm7zp62tF21tvRjUbww3b8QRfU57sd7v82E4t+5Mm+aevPX2\nm7h7tjVa3mm/jqdrpz40a+RCZy933n/f8P+we08vkpPvYVPfUdu+TTTsW0yZOtagb5GhvVsPbFu0\np41tJ6NkzZzZFH24+XNX0LyRCw6tOtK4SUPaOBpnUNjMzIyfZ/zAJ15f0rqJBx293LLl/aSHF8nJ\nKTRr2I7FC9bw/XhtXo1Gw/wl0xg5dDy2zTzolKlvsXDeClo1dsOxdScaNWmAg2Mro+TNKf+AyQP4\noecPfOXwFXYd7KhUvZJBmT/i/uDXob+yz3ufweONHBpRrXY1BrQdwGCPwXTu15lixYuZLGdBqxem\naCsAXNwdefjAuH3OjMyTpn1Hr679cWzuSftOLtkyd9Nltm3kzvKFaxk9bjAA3tsCcLXriqtdV4b0\nH8utG/FEn7tE6TdL8d2EoXzSsS9OLTrx9jtv0aK18YY4zMzMmDjtO3p3/Rrn5h1p36kd72XJ3LV7\nR1KS72HfyIPlC9fpM7t2cKJQoUK4tPLCw+FjPunlRfmKVjz55wmfePbB1bYrbrZdsW3Tgvo2dYyW\nOTc8XZ1YNHPyywuakKn6nQDtO7Q1+vVTRubZsyfj0b4H9erZ061bB2p+YNjX+Oyzj0hKTqFWrZbM\nmbOUKT99B8Djx/8wfsJ0Ro2elO11/fz30KKlu9HzZs3+/dTh9P9kCO1bfYxrR2eq1qhiUCYh7jbf\nD5pEQJZ+8uNHj/num4l42n7CVx8NZtSkwZQoWdykecUzqqoW+J/8YrKBdkVRmgHuQENVVesCjsDN\nf7GdKW7QOktV1fpAF2CFoij/6n2bKMsLNbSpy7Wr17kee5PU1FR2bvfHxc3wkzsXtzZs2qj9xH+X\ndxCt7Jples6R67E3uXTxssE25ubmFClaBI1GQ7FiRUlM/MMk+es2/JAb125x63o8qalPCdgZjEM7\nww5e/M0EYqIvk56enm37o4ciTHJyyqyBdR1ir97kxvVbpKam4rMjgLau9gZlnF0c2LpRO5PX3yeY\nlrZNAbB1aM6F8zH6zmFSUgrp6ekoioKiKBR7oygAJUq8we3EP42WuaF1XWKvXud6rDaz9w5/XNwM\nZ8i6uDqweYO2Xvh676ZVDp+wZ/XGG8XoP+AzZk5faLSs+swmqMs13q/GyYgoHj16TFpaGofDj+Pm\n7mSUvNY29bh69Tqxurw7tvnhliWvq5sjG9ZrZ7p57wzEVpc3Iw9AkSKFc2zU7eyac+3qDW7ejDdK\nXsioyzee1eXtgbR1NZwt287VgS0bvQHw8wmmlb4ut+DCuex1GaDYG8X46utezJ6x2GhZQds+XI+9\nyc3rcaSmPsXfO5g2LnYGZRxdbNmx2Q+AIN9QmrVqDMB771fl8MHjAPx1J4l7KX9Tp34tg20rV63I\nW2+/ScSR00bN/W/fi6OLrUGZuJsJXIq+TLqava0zpYy6fF1fl/1xzXbsObJxvfbY89kZpK/LAK7u\njly/dpOLF3432OZweARJSclGz2tjU5+rV54de9u2+eLu7mxQxt3NmfXrtgOwc2cAdnbNAYiKOk9i\ngvZ8Fh0dQ+HChSlUSPvNjJ49uzBj+gJA21G8ezfJ6NkB6jSsxc3M5z3vEOyfc95TczjvHTt0wuTn\nPTDdfs5QrVoV/ve/twgPP26kvIZt8vZtftnaezd3Rzau1+b13hmoz2vQJhcuTEaTfDvxT6IizwPa\nDwkuXbqMlVW5POVs3KgBV67Ecu3aDVJTU9m8xQcPD8NBZQ8PZ9au3QrA9u3+ONi31D3els1bfHjy\n5AmxsTe5ciWWxo0avPDvXbx4mZiYK3nKnFl96zrEXss4jzzFZ0cgzi6G5xFnVwe2bsrUJ8phUKlD\nZ1d8tgfqf7//t/YDfHNzcywsLIx2sWOdpW+xY7s/Lu6G/SFXN0c2bdCeq328g2idpX2Ljc3evpmS\nKfpwjx49JvyQ9sPD1NRUzkRFY1m+rFHyNrCuy7VMfQvv7QHZ+hZtXR3Yousn+/ns1veT7RxaEH3u\nUqa+RTLp6em6vMf1ec+eicYyj8fe89SoX4P42HgSbyTyNPUpB3YdoKlzU4Myf9z6g9iLsdnqZaXq\nlTh77Czpaen88+gfrkVfw9rO2iQ5C1q9MFVbUeyNovT9uiezfzVunxOgfsPaxF67oe+r+e4MwsnF\n8JrPycWO7Zt2ARCwKyTHQfP2nV3YtUObuVKVCly7cp2/dH2KsANHcfEw3izbeg1rc/3azSyZ7bJk\nttdnDtwVQvPW2r6yqqoUK1YUjUZDkSKFSX3ylPt/a+cGPnzwCABzC3PMzc3Jr3sc2tSvQ6mSJfLn\nj2dkMFF/6I03ivHtt3345Ze5Rs/cqFF9g77Gli0+eHgYZjboa+zwx17X13j48BGHD0fw+PE/2V73\n+PFTJhsXylCnYS39+NDT1KcEeoe8YHzIsGJev3qTG9e0Q4h/3r7DX3eSePOtN02aVwhjMOWMdkvg\njqqq/wCoqnpHVdV4RVEaKYpyWFGUKEVRjiuKUkJRlN6KomxVFMUXCAZQFGWEoigRutnw+rUuFEXp\nrtsuUlGUxYqiaHSP31cU5Sfd6x5VFCVbr0JV1QvAU+BtRVEqK4oSqnv9UEVRKuleZ5WiKDMVRdkH\n/KIoSnFFUVYqinJWV7Zzpiwv/HuvtNMsyxJ/K1H/e3x8IpZWZbOVibuVAEBaWhr37v1NmTJvUqxY\nUQYO6cv0qfMMyicm3Gb+3OVEnt/P+d/DuXfvb/bvDTdG3GzeKfc/EuNu63+/nfAHERqkvgAAIABJ\nREFUZS3/Z5K/9arKWZYlPi5B/3tC/G3KWRru43JW7xAfp/1/yNjHb5YpTdVqVUBVWb9tCUH7t9J/\noHZ259OnTxkzbBKhYd6curCf6u9XY+Pa7UbLbGlVlri4TPUi7jaWWTNbliUuLnu9AKhUuQJ7D+3E\nx38tTZs9u2AYPXYQC+at4NGjx0bLqs9sgrp8Ifp3mrWw4c0ypSlatAiOzrZYVbA0Sl4rq2dZAOLi\ncshrVc4wb8rflNGd7K1t6nE0IpDDxwIYMugH/SBPhk5e7mzbZtzlebT/58/2cUJ8IuUs38lWJmtd\nLlOmNNXeq4yKysbtSwg+sI2vBz6bqTxq7Lcsmr+Kh48eGTnvOyRkah8S429nax/KZmpD0tLSuH/v\nPm+WKc3FczE4utih0WioUMmK2vVqZrt49OjYDn/vEKNmfp6ylu8YtHWJ8X9QNsu+zy+WWepyfA51\nOXN919bl+5R5S3vsDRryFb/8bPyLhOexsirLrbhnH0DFxSXkmDejTEY9fitLR9vT04UzUed58uQJ\npUqVBODHH4cRftiPtevm8847b5skf9ly75AQn+m8F/8HZcu9Xuc9MM1+zqxL1/Zs3+ZntLyWVuW4\nZVCPE7CyzNoml9WX0bdvurw2NvU4FhHEkeOBDB74fbY2uVKl8tSt9yEnIiLzlNOqfDlu3TLcr+Wz\nDCBalS/HzVvP9mtKyj3eeutNyltl39aqvHZbVVUJDNjIsaOB9Pni0zxlfBFLy3dIyHQeSYy/jWW2\n88izMtr9fD/b8ngeHdvhs8NwWal12xYTGXOAB/cf4O9jnG9zWVqW0/d1QNe+5VAv4m5lymvQvn3J\ntBzaN1VV2e69kr0Hd9Lrs25GyWqQxwR9uAwlS5XA2cWeQweOGCev5bM+MGj7ydn2caa+dFpaGn/r\n+hZV36uCCmzcvpTgA9sZMDD78iIlS5XAuZ3x8mb1drm3+TP+2WSXOwl3eKvcW/9q22sXrmFjZ0Ph\nIoUp+WZJ6jary/+sTNOeF8R6YYq2YsR337Jk/moePTT+tUg5y7IG/U7tNV8O/eT4Z/3Ov3PK7NlW\n/+FA7NUbVKv+LhUqWqHRaGjr6mDUD43KZdvPf2S7Ti1r+Q4J8c/2c0bmwF17ePjwEcei9xAetZul\n81eTknwP0M4q9t+/mRMX9xF24CiRJ88aLXNBY6r+0I8/DmPOnGU8NEFdLm9lya2bhtepVuUts5Qp\nZ9AnSrl3L1vm/PBOuf+RGP9sMP92/B+88wr95NoNamFhYcHN2FvGjCeESZhyoD0YqKgoSoyiKAsU\nRbFVFKUQsBkYpKpqPbSz3DNGcpoBvVRVdVAUxRmoDjQG6gPWiqK0VhSlJtANaKGboZ4GZFx9vAEc\n1b3uQSDbQne6pWvSgT+BecAa3Wz79cCcTEVrAI6qqg4DfgBSVFWtoyubsSbBS//eq1AUJdtjWWdb\n5FgGlVHfDWTR/FU8yPLVu1KlS+Li2gbrOg7UrtGSYsWK0aWbUVbQySbn/Cb5U68sh4jZ9zE5FkJj\nrqFR04Z88+VIPF164OLWhpatm2Bubk7Pz7vR1taLhjXtuHA+hm9zWGvx1TO/Yr1QVW4n/kGDD+1x\naNWRH8ZOZdGyXyle4g1q1/mAd6tWIsBvj9FyGiXzC+ry7zFXmDNrKdu9V7Jlx3LOn71I2tOn2V7D\neHmzlsm+XcZ7OnkiiqaNXLC37cjQYf0M1ju3sLDA1a0N3juNu7Z1zvvvX5RRVTQacxo3bciAviPp\n0K47Lu6OtGzdlA/rfECVqpUI9As1alZdmByz/Ju82zbsIjH+Njv3rGXs5GGcioji6VPDgTO3js74\n7TDOshUv82/akfzyb4695/1fjB47iIXzV2Y79kwpL3kz1KxZnUmTR/Ptt9qvyZqba6hQwYojR07Q\nork7x4+dYsqU74wbXJ8t+0Nqfk0VewFT7OfMvLw82LJ1V96DPj/Kvz5XA5w4EUWTRu2wa+3JsOH9\nDdrkN94oxtoNCxg9chJ//523lQBf/fz84m1t7Txp3KQd7h7d6d+/Ny1bmmj1xTy0yxkaWNfh8aNH\nXLpg+G3K7l5fYV3TnkKFCxltaYV/VS+e274NZOG8nNs3F6ePsG/lSddOX/BF309p1qKRUfK+KM+/\nKfO8PlwGjUbDkuUzWbZoLdeNNPjwvL7Zy/OCuUZDk6YNGdB3BB3afarvW2TOu2jZDJYtXseN6yYa\nLMmhjvzbJvnUwVOc2HeCX71/ZdS8UVw8dZG0LH0NYylo9cIUbUWt2u9T5d1KBPmboM8JOZ+fs2V+\ncZn61nV49OgxMbpv2N5L+Zuxwyczb/l0tvmv4taNOJ6mGedaRJvn1fdzvYa1SUtLo+mHTrRu6Eqf\nAT2pWFm7rn96ejpudt1oVseZeg1qUyOPy6YVZKboD9WtW4uq1Srju8s0y0Xm5dyX3/7N9erLvP3O\nW/w8bxzfD570WrwnIV7GZAPtqqreB6yBL9EObG8GvgISVFWN0JW5p6pqxpkpRFXVv3T/dtb9nAZO\nAR+gHXhvo3vNCEVRInW/Zyxa9gTImEp1EqiSKc4QXfkZQDdVe3Q2Azbonl8LZL4TzlZVVTN6VY7A\n/EzvK+O75y/6e3qKonypKMoJRVFOPH6SklMRA/HxiVhVePapuJVVOf3XkzKXKa+bxavRaChZsgRJ\nfyXT0KYe4yaO4NTZvXzVvxeDh/fjiy+7Y2vXnOvXb3H3bhJPnz7FzzeYRk1e/NXkV3U74Q/KZZpl\nWtbyHf4w4hIqxpAQf9vgE2BLq7LczvKVKW0Z7f+Dfh8npZAQf5uj4SdI+iuZx48eszfkELXr1eLD\nOh8AcD1W+9UmX+8grJvUN1rm+LhEypfPVC/Kl832Na+E+ETKl89SL5KSefIkVb/cw5nI88Reu0G1\n997FpnED6tWvzckzofgFbaDae1Xw9ltjvMwmqMsA69duw6F1RzxcPiUpKYUrV64bJW9c3LMsAOXL\nlyMx4bZBmfi4LHlLafNmFnPpCg8ePqJWrWc3eXJytiUq8jx//nHXKFkzaP/Pn+1jS6ty3E7IXi9y\nrsuJHAmP4K+/knn06DF7Qw5Sp14trBvVo269Dzl+JgSfwHVUfa8K2/1WGSVvYvxtg1no5azK8kfi\nHcMymdoQjUZD8ZLFSU5KIS0tjSk/zKS9/Sf07zmMkiVLcP3qDf12H3xYHY25hvNnLhol68vfi2Fb\nV87q9Wnr4rPUZavyORx72epycZL+SsamUT0mTBpJ1Pn99P+6N0OH96fvV9lvWmRMcXGJVCj/7EZ6\n5ctb5pg3o0xGPf5Ld+xZlS/Hxk2L6dtnKNeuaevE3btJPHjwkF26C54dOwKoV7+2SfLfTvjDYCZU\nWat3stXr14Ep9nOGOnVqYm6uIfL0OaPljY9LpIJBPbYkITH7OaRClnPIXzm1yQ8e6ttkc3Nz1m1Y\nwJbNu4xyQRx3K4EKFQz3a3yWc0fcrQQqVni2X0uVKslffyVxKy77thnfjkjQvcaff97F2yeQRo2M\n16fILCH+NpaZziPlrMqSmKUty1xGu5+17XKG9p1c8M60FERm//zzhODAfbTNslzDq4rP1NcBXfuW\ntV7EJVK+Qqa8uvbN2qYe4yeNJPLcPvp93Zshw/rRR9e3yHiNO3f+wt83BGtr49xoVp/HyH24DDNn\nT+LqlVgWL1xtvLyZ+sCg7Sfn1H+zypS3hC5vfPxtg75FaMhB6tZ7tszbjNkTuHr1OksXGq+/mdWd\nhDsGs9Dftnybu7f/ff9r09xNfNPuG8Z+OhYUiL9mvCX/Mito9cIUbYV1o/rUqVeLI5G72Rm4hqrV\nqrB110qjZc7a79Re82XPbGX1rN9ZIktmj47t9MvGZAjdfQBP50/p2K4HVy7HEnvF8JyYF9n38zvZ\nrlMT42/rZ9FnztzBy4WDew/z9OlT7t75ixPHIqlb/0ODbf++9zdHwyOwbdPcaJkLGlP0hxo3aUiD\nBnWIvhDGntCtvFf9XQKDNhkt8624BCpUNLxOzfhWg0GZTH2iUiVLZusT5YfbCX9QzurZN0nKWr3D\nn7m4ZnqjeDEWrJ/J3KmLOXPyvCkiiudIRy3wP/nFpDdDVVU1TVXV/aqqjgO+ATrx/A+wMt+NUQF+\nVlW1vu7nPVVVl+seX53p8fdVVR2v2yZVffbxVhqQeX31WbryrVRVPfS8uC/IklPmF/29Zy+qqktU\nVbVRVdWmSKFSz/nTz5w+eZaqVatQqXIFLCws6NjZjaAAw0/5gwL28tHH2hsDtvdsp/9KoEe7T2hY\nx4GGdRxYvHA1v81YxPIl67h1Kx6bRvUpWrQIAK1tmxFz6epLs7yKs6ejqVy1IuUrWWFhYY5rR2f2\n7X7eLs8fkafO8W61SlSsVB4LCws6dHIlONDwZkjBQfvo8nEHANw6OBN+ULve4YHQcGp+WEO/3n3T\nFjb8fukKiQm3qf5+Nf1X1lvbNeeyEffx6VNnebfas3rh2cmNoADDGz4GBeyl2yfaeuHh2Zawg9ob\npb311pv6m6tVrlKBqtWqcD32JquWb6TOB62wrtsG93afcOVyLJ7uPY2X2QR1GeDtt8sAUL6CJe7t\nndlhpKUKTp08Q7VqVaisy9vJy52ALHkDAkL55FPtTdI8O7pwUJe3cuUKaDQaACpWtKJ69Xe5fuPZ\nDCKvLh5s22rcZWMgoy5XpmJlXV3u7MLuLHV5d+A+un7sCYB7B2fCdHV5f2g4tT58n6L6utyImEuX\nWbNiMw1q2tG4rhMdXLpz9XIsnd17GyXv2dPRVHm3IhV07YObpzOhQQcMyoQGHaBTN+1Nedp5tOFo\nWAQARYoWoWgxbRvWwrYJaWlpXI65pt/OvVM7/Hb8dzcdzfm9ZL+5Xn7Q1uXK+mOvk5cbgdmOvVA+\n/lR77HXo2I6DB7Tthavzx9T70I56H9qxcMEqZs5YyNLFa02a9+TJKKq99+zY8/LywN/fcAkg/4AQ\nPu2uXbmtY0dXDhw4DECpUiXZsX0l436cxtGjJw22CQgIpbVuJqW9fQsuXjTNmsznTl+gUtWKlK9k\nqT3veTqxb/frURcyM9V+BujSpT1bjdzGnTx5hqqZ2uTOXu4E+Bt+AyvAP5SPP9Xm9ezowoHntck1\nqurb5PkLp3Lp0hXmz11ulJwRJyJ57713qVKlIhYWFnTr2gE/P8NlUvz8gunRowsAnTu7sW9/uP7x\nbl07UKhQIapUqch7773L8YjTFCtWlOLFtbNTixUripOjLefPX8IUok6d492qGX0iczp0ciEkyPA8\nEhK4jy4fZeoTHXp2Y2FFUXDv4GwwEFXsjaK8U1a7VJNGo8HBqTWXf7+GMZw6eZaqmfpDnTq7ZZsN\nGxgQykefaM/VHTzbcUjXvrm1/YT6te2pX9ueRQtWMevXRSxbsi7b/rZv05IL0TFGyQum6cMBjPl+\nMCVLFWfs6ClGywoQeeosVatVppKub+HZOYd+cuA+uur6ye4d2hKuy7s/NIyamfoWzVo0IuaS9p4C\no8YOokTJEvww+mej5s0qJioGqypWlK1YFnMLc2zb23I05N/dPNjMzIwSpbXrR1f5oArv1nyXkwez\nt3nGUNDqhSnairUrN2PzoQPN6relo0tPrl6JpUv7z4yX+fR53q1aWZ/Zo2M7QgL3G5TZE7Sfzh9p\nv+nt2t6Jw4ee3WdEURTcsmQGeEt3LVKyVAl6fN6NTet2GC3zmdPnqVK1EhUyZd4TaNhXzpzZpb0T\nR3SZ424l6u9tVLRYURrY1OHK79co89ablNCti164SGFa2jblyu+xRstc0JiiP7Rs6Treq9aEWjVb\n4timC5d/v4ZLu4+MlvnEiSiDvkbXrh3w8zPM7OcX8qyv0cmN/ftNs1RwbmXuJ5tbmOPi6fSvx4fM\nLcyZveoXdm0NINh378s3EOI1YbKbfSqK8j6QrqpqxpVtfeAC0E5RlEaqqkYoilKCZ0vHZLYbmKQo\nynpVVe8rilIeSAVCAR9FUWapqvqHoihlgBKqqr7KlNbDwEdoZ7N/CoQ9p1ww2g8JBuve15uZZrUb\nXVpaGqNHTGTrzuWYaTRsWLuNSxcvM3rsQCJPnSMocC/r12xlwZLpHI8MITkphb6fDXnha546cQZf\nn93sPeTN06dPOXvmAmtWGu8T1qz5J4+ezrLNczDTmLFjgy+XL13l21Ffci7yAvt2H6J2/ZrMXTWN\nkqVKYu/cim9HfolHa+2JaO2uJVR9rzLF3ijKvkhfvh/yE+H7/l3nODcZvx/5Exu2L8FMY8bm9TuJ\nuXiF4WO+ISryPCGB+9i0djtzFk0l7GQgyUkpfP3FcABSUu6xZMFqAkI3o6KyN+QQocHaAZVZ0xaw\nw381qU+fEnczgSFfG2+ZgrS0NMYMn8iWHcsw02jYuG47ly5eZtR3A4k8fY7dgXtZv3abtl6cDiYp\nKYUvP9fWi2YtGjHqu4E8fZpGenoaw4eMM5ipYSqmqMsAK9fNo0yZ0qSmPmXksAn6tQeNkXf4sAns\n8F6FRmPGurXbuHjhd777fjCnT50lMCCUtau3sGTZr5yO2ktSUjKf9x4EQNNmNgwZ9hWpqU9R09MZ\nNmSc/iZJRYsWwd6+BYMHjjVKzqyZvxvxExu3L0WjMWPTup3EXLzMiO++Ier0eYID97Fx7XbmLv6F\nw6eCSE5Kpt/nz+ry4vmrCdy7BVVVCQ05qK/LppKWlsaEMdNYsWUeGjMN2zb6cPnSVQaN6sfZyGj2\n7j7I1vU+zFgwiT3HvUlOSmHIl9rj6K2332TFlnmo6SqJCX8w/OsfDF7btb0jfT4eZNL8Wd/LxDHT\nWb5lru697OLypasMHPUV5yIvsHf3QerUr8X81dP1bd3AkV/i1sq4a/8+L9vIYRPY7r0SjUbD+rVb\nuXjhd8Z8P4jIU+f0dXnRsl85GRVKUlIyX/Qe/NLXXbZyFi1aNeGtt97k3KUwpv40m3Vrthol77Ch\nP+Kzaw0ajYY1a7Zw4cLvfP/DEE6dOkuA/x5Wr9rCsuUzOXN2P0lJyfTq+S0AX/XrSdVqlRk9ZiCj\nxwwEoL1HD/788y4/fD+VZctnMm3aj9y58xdffTUiz1mfl/+nMTNYskl73tu50Zcrl67xzcgvOR/1\n7Lw3e+U0SpYugZ1zKwaM6EsH248BWOOzmHd1573Q0778OGQy4fuPveSvvlpOU+xngE6d3ejU0XiD\nIxl5Rwwbz06f1Wg0Zqxdo63HY78fzCldm7xm9WaWLJtJ5Jm9JCWl8FkvbbZmzW0YMrQfqU+fkp6e\nztDBP/LX3SSaNrPh4086ce7cRcKOaD+knTh+BsG79+cp56DB3+PvvwGNmRmrVm8mOjqGceOGc/Jk\nFH5+IaxYuYlVq+ZwITqMpKRkPu3+NaC9kdrWbb6cidrH07Q0Bg4aS3p6OmXL/o9tW7UfBGjMNWza\n5E1wsDZjhw7t+G3WZP73vzL4+KwhKuo8bu6vvoZ7WloaP4ycwvptizHTaDL1iQYQdfo8IUH72bRu\nB7MX/UzYiQBtn6jPs2OpaXMbEuJvGywDUqxYMVasn0fhwoUw05hx+OAx1q7c8soZs+YdOXwC27xX\noDHTsH7tNi5evMyYsYM4ffosQQF7WbdmK4uWzuBE5B6SkpLp85K+xf/eeZu1G7RfXjU3N2fbFl9C\n9xhvkogp+nCWVmUZOqI/MZeusPeg9maZy5euY92abUbJ+92IyWzcvgyNxoyN63Zw6eJlRn73LZGn\nzxEcuI8Na7cxb/EvHDkVRHJSCl99PgzI6FusImjvVn3fYk/wASytyjJkRD9iLl0h5KD2HkYrlmxg\nw9q8580qPS2dhT8sZPK6yWg0GoI3B3Mj5gY9hvUg5kwMx0KOUaNeDX5Y+gPFSxWniWMTug/tTj/H\nfmgsNMzYPgOAh/cfMn3gdNLTTHND84JYL4zdVphaWloaP46awpqtC9FoNGzZ4M3vl64wdPTXnImM\nZk/Qfjav28mshVM4EOFHcnIK3/QZqd++SXNrEuJvc/N6nMHrjpsyilq1awAwe/pirhnp27UZmceN\n+pk1WxdipjFjqy7zkNFfczbyPHuCDugy/8S+CF9Sku/xrS7z2uWbmD53IrvDd6AosG2DDxejf+eD\nWtWZMX8yGo0ZipkZ/t7B7DVxn/95RoybSsTpMyQn36ONZ3e+/qIHnbPcQNzUTNkfMmXmwYN/wN9v\nPWYaM1av2kz0hRjG/Tick6e0fY2VKzexauVsoqPDSPorme49vtZvH3PpCCVLlqBQIQvae7TFze0T\nLlz8nZ+njKVbN0+KFSvK1SsRrFy5kUmTZxo9+5QxM1i8aTYajRk7N/px5dI1Bozsy/moi+zX9ZN/\nW/mLrp/ckgEj+uJp+wnt2jti3bQBpd8shWc3NwDGDpzEpfP/3Q3NhXgViqnWOFIUxRqYC5RGewPS\ny2iXkXlX93hRtIPsjoAXYKOq6jeZth8E9NH9eh/orqrqFUVRugFj0M7GTwUGqKp6VFGU+6qqFtdt\n6wW4q6raW1GU8cB9VVVnZMlXBVgBvI12aZvPVFW9oSjKKsBPVdVtunLF0S4dY4125voEVVV3PO/v\nvWifvF2yRoFaUOp/RUq/vNBr5l7qg5cXes08STfeun7/hYK4LlpqumnW1zSlYuaF8ztCrhS3KJrf\nEXItx/WdX2N/Psr/r3/mVkFr3wCqlDDKvc3/M7F/3355odeMRjHpFypN4lHqP/kdIVfKFs//G6Dl\n1sMCto8BNGYFqy4XxGOvQYkq+R0hV07cM823hk2psMYivyPkWkGry2YFrM8JEHNpZ35HeCWlKznk\nd4R/LdWI6/v/V2qUrpDfEXLt3O2jBe8AfA3UK9e84A38ZBGVeDhf/u9NNtAuspOBdtOTgXbTK4ht\nhgy0m54MtJueDLT/N2Sg3fQK2gAJyED7f0EG2k2vIB57MtBuejLQbnoy0P7fkYF205KB9v9/1C3X\nrOAN/GRxJvFIvvzfF6wzlBBCCCGEEEIIIYQQQgjxmpGBdiGEEEIIIYQQQgghhBAiD2SgXQghhBBC\nCCGEEEIIIYTIA/P8DiCEEEIIIYQQQgghhBAi/6UXwHvzvS5kRrsQQgghhBBCCCGEEEIIkQcy0C6E\nEEIIIYQQQgghhBBC5IEsHSOEEEIIIYQQQgghhBACFVk65lXJjHYhhBBCCCGEEEIIIYQQIg9koF0I\nIYQQQgghhBBCCCGEyAMZaBdCCCGEEEIIIYQQQggh8kDWaBdCCCGEEEIIIYQQQghBuiprtL8qGWj/\nD93752F+R8iVlMcP8jtCrpUt/mZ+R8i15Ef38ztCriiKkt8Rcs1MKXhf3nnw5HF+R8iVe+YFq30D\nKGQmp0BTK4gdtLv/3MvvCLlSolBR/n7yKL9j/J9XpmiJ/I6QK7fvJ+V3hFwrXbR4fkfItUepT/I7\nQq5YmGnyO0Kunbp3Lb8j5IoZCo/TUvM7Rq7cL2B9ToC3ClibnAYkPS5Y13wFVfKNvfkdIVda1f08\nvyPkSszfcfkdQYjXnowyCCGEEEIUUDLILoQQr4+CNsgu/hsFcZC9dCWH/I6QawVtkF0I8X9TwZvm\nKYQQQgghhBBCCCGEEEK8RmRGuxBCCCGEEEIIIYQQQghUCt4SoK8LmdEuhBBCCCGEEEIIIYQQQuSB\nDLQLIYQQQgghhBBCCCGEEHkgA+1CCCGEEEIIIYQQQgghRB7IGu1CCCGEEEIIIYQQQgghSFdljfZX\nJTPahRBCCCGEEEIIIYQQQog8kIF2IYQQQgghhBBCCCGEECIPZKBdCCGEEEIIIYQQQgghhMgDWaNd\nCCGEEEIIIYQQQgghBCqyRvurkhntQgghhBBCCCGEEEIIIUQeyEC7EEIIIYQQQgghhBBCCJEHMtD+\nmnN2tuPc2QNER4cxYviAbM8XKlSI9esWEB0dRtghXypXrgBAmTKlCd69hb/uXuK33yabJte5g1yI\nDmPEiOfkWr+QC9FhhIc9ywUwcuQ3XIgO49y5gzg52eof/z3mKKdP7eFERDBHjwToH5/68/ecPXuA\nUydD2Lp1GaVKlcxzfrs2LThwzJewEwEMGPRFDvktWLB8BmEnAvAN2UCFilYAdPRyY/eBbfqfG3fO\nUKv2+9r3NXYgx8/u4dKN43nOl8EU+7lUqZJs2rSEs2cPcObMfpo2sQagc2d3IiP38s/jm1g3rGuc\n7EauuxMnjOTK5eP8dfdSnvPlxMnJljNn9nH+/EGGD/86x8xr187n/PmDHDzoY5B59+5N3LlzgVmz\nJhps07Vre06cCCYiYje7dq3hrbfezHPOts52nD93kIvRYYx8Tr3YsH4hF6PDOJylXowa+Q0Xo8M4\nf+4gzrp6UbhwYY6E+3HyRAhRkXsZ9+Mwffkli2dw8kQIp06GsHnTEt54o1iesjs52XI6MpQzZ/cz\nbFj/HLOvXjOPM2f3s/+AN5UqabM7OLQkLNyX48eDCAv3xda2mX6bLl3ac/x4EMeOBeLts9oo+/h5\n2ji25vipYE5GhTJ46Fc55l++ejYno0IJ2beNipXKGzxfoYIlNxOj+GZg9nbHVApCZicnW6Ki9nLu\n3AGGD8+5XqxdO49z5w5w8KBhvQgP9yMiYjfh4X7Y2jYHoGjRIuzYsZLIyFBOngxh0qRRRs9s36Yl\nYREBHDkVxDeD++SQ2YLFK2Zy5FQQAXs2UbGSlf65mh/WwC94IweO+LIv3IfChQsBMPr7QZw8t5cr\nt04YPS+Y5vjLsGXrUiIidhs1r6NTa06e3kPkmb0MGdYvx7wrV88h8sxe9u7fQSVd3bW2rkvYET/C\njvgRftQfdw9ng+3MzMw4dNiXLduWGTUvaOtF+IlAjp7ezbdD+uaQ2YIlK2dy9PRuAkM364+3ipXK\nE5sYSeihnYQe2sm0WeP123Ts7Mb+w7vYF+7Dxu1LKVOmdJ4yGrtvUaNGNU5EBOt/7t65yMBvtcfE\nDz8MJfbaCf1z7do55Ck7gEObVhw5EcTx08EMfM4+XrpyFsdPBxMUusVgH9/qHylHAAAgAElEQVRI\njGLfIW/2HfJm+qwJgLa92LBlMYcjAjl01I8fxg/L9pp55ejUmlORoUSd3cfQ59Tl1WvmEnV2H/sO\n7HxWl23qcfioP4eP+nPkaAAe7Z/V5QWLfuFabATHI4KMnheMf+4oXLgQe/Zv59ARXw5HBDJ67CCj\n5jXFsWdhYcGM2RM5fDKIsIgA3No7Z3vdvGjj2JoTp0I4HbWXIc/ZxytXz+F01F5C923X14uG1nU5\ndNiXQ4d9CTviZ9DGfT3gM45GBHLkeCDLV/6mP78YQ0E8VxeUa77MCtq5uqDlzY3vp8yktdtHeHbP\n3m7np6Z2jdl8aA1bw9fT45tPsj1fv0ldVu9eQtiNUOzdbA2eC78ZypqQZawJWcb0VT+ZLGMbx1Yc\nO7WbE5F7GDT0y2zPFypUiOWrfuNE5B5C9mY/h5SvYMmNhEiD64/Ic/sIO+rHgfBdhB7YYbLsQuTV\n/4mBdkVRVEVR1mb63VxRlD8VRfF7yXZlFUXxUxQlSlGUaEVRAl5SvoqiKOee89x+RVFsXu0d5MzM\nzIzZsyfj0b4H9erZ061bB2p+UN2gzGeffURScgq1arVkzpylTPnpOwAeP/6H8ROmM2r0JGNG0uea\nM/snPDy6U7eePR9186RmTcNcn3/2MclJKdSs1ZLZc5YyZcpYAGrWrE63rh2oV98Bd/dPmTtnCmZm\nz6qho1MXbBo507SZq/6xPaEHqV/fgYbWTvz++1VGjfomz/knT/ueHl37Y9+sPR06u1L9/aoGZT7q\n3omU5Hu0tHFl6cK1fDd+KAA7t/nT1taLtrZeDOo3hps34og+px303bN7P+6OH+UpW9acptjPs2ZO\nJHj3PurUscXa2okLF38H4Pz5i3Tt2pdDh44aJbsp6q6f/x5atHTPc74XZe7QoRf167eha9f2fJAl\nc+/e3UhOTuHDD1szd+4yJk8eo888YcKvjB5t2FnRaDTMmDGetm270ahRW86evUj//r3znHPO7J9w\n9+hOnXr2dHtOvUhKSuGDWi35bc5Sfs5UL7p27UDd+g64ZaoX//zzD47OXbG2ccLaxpm2znY0adwQ\ngGHDx2Nt40RDaydu3ohjwNef5Sn7zFkT6ejZG+uGTnTp0p4PPnjPoEyv3l1JTk6hbh075s1dzqTJ\nowG4ezcJL68vaNy4HV/2Hcay5bMA7T6ePv1HXFw+pkkTF86dvcBX/Xq9csaX5Z8+czxdOn1BU5t2\ndO7izvtZ8vfo1YWU5BSs67Vh4fyVjJ800uD5n34Zy56QgybJV1Azm5mZ8dtvk+jQoRcNGjjq6kX2\nYy8pKYXatW2ZO3c5P/2UuV58TqNGbenbdygrVszSb/Pbb0uoX78NTZu60qyZDc7OdkbN/POMH/jE\n60taN/Ggo5cbNd6vZlDmkx5eJCen0KxhOxYvWMP344cD2jo7f8k0Rg4dj20zDzq59yI19SkAwUH7\ncWnTzWg5s2Y29vGXoX2Htjy4/9DoeX+dOYHOHT+jkXVbvLp4ZKu7PXt1JTn5HvXrOjB/3gom6AZp\noqNjsG3ZgZbN3Onk2ZvZcyej0Wj02/Uf8Bkxl64YNW9G5qm//sgnXn1p1didjp1zqBc9vUhOvkfT\nBm1ZvGA1P0x4Nqh7/doN2rTqSJtWHRk5ZDygrS+Tf/mOTu49sW/Rgejzl/j8y+55ymjsvkVMzBVs\nGjlj08iZxk3a8fDhI7x9AvWvN3vOUv3zQUF7Xzl7Rv6pv/7IR159aNHYjY6d3bPt4097diE5+R6N\nGzizaMEqfpwwXP9c7LUb2LfyxL6VJyOGjNM/Pn/uCpo3csGhVUcaN2lIG8fWecqZNfPMWRPp5Nkb\nm4bOLzz26tWxZ36mYy/6/CVatWhP86ZueHr2Ys6cn/R1ef3a7Xh69jZazqyZjX3u+OefJ3Rw60Gr\nZh60buZBG8dW2DSqb7S8xj72AAYP78edP+/S3LodrRq7cSTMeAOr2jZuPF6dPqexTVs659jGdSE5\nOYUG9RxYMH+lvo27EB2DXStPWjX3oLPnZ/w2R9vGWVqWpV//Xti18qRZYxc0GjM6e3kYLW9BPFcX\nhGu+rJkL2rm6IOXNLU9XJxbNNP6kxbwwMzNj+JRBDPl0FB/b9cK5gwNVqlc2KHM77g8mDZ5K8M49\n2bb/5/ETejr1oadTH0b0HmuyjNN+HU/XTn1o1siFzl7uvP++Yb3ormuTbeo7as8hE0cYPD9l6lhC\nc7j+aO/WA9sW7Wlj28kk2cUz6apa4H/yy/+JgXbgAVBbUZSiut+dgLh/sd1EIERV1XqqqtYCRpsq\n4Kto1Kg+V67Ecu3aDVJTU9myxQePLLOyPDycWbt2KwDbd/hjb98SgIcPH3H4cASPH/9j9FyNGzUw\nyLV5iw8eHm2fn2u7Pw66XB4ebdm8xYcnT54QG3uTK1diadyowQv/3p49B0lLSwPg2LFTVChvmaf8\n9a3rEHvtBjeu3yI19Sk+OwJxdjGcYeXs6sDWTT4A+PsE07J1k2yv06GzKz7bn11Injpxhj9u38lT\ntsxMsZ9LlChOy5ZNWLFyIwCpqamkpNwD4OLFy8TEGGfgwVR19/jxUyQm/mGUjC/LvHWrb46Z163b\nBsCOHQHY27cwyPzPP48NyiuKgqIo+lngJUsWJyHhdp5yZq0XW7b40D5LvWj/nHrR3qMtW55z/D14\noO3IWliYY25hgao7Mf3993396xYpWkT/+KuwsanP1SvXiY29SWpqKtu2+eLubriP3d2cWb9uOwA7\ndwZgZ6ed9RQVdZ7EBO3/fXR0DIULF6ZQoUIoigKKQrFi2n1comSJPO/j57G2qcfVq9e5rsu/Y5s/\nrm6OBmVc3BzZuH4nAD47g7C1ezY7x9XdkevXbnLxwu8myVdQM2ccexn1YutWX9zdnQzKuLs7sX69\ntl7s2BGAnZ322IuKOk9CDvXi0aPHHDx4BNC2c5GR5yhfvpzRMjewrsu1qxnnkVS8twfQ1tXwPNLW\n1YEtG7XnET+f3bS0bQqAnUMLos9d0l+wJyUlk56eDsCpE1H8cftPo+XMzBTHH8AbbxTj22/78Msv\nc42cV1t3M/Ju3+aHW5Z64ebuyEZdvfDeGajP++jRY32/oUjhwmRutqysytG2nT2rV202al7QzjK9\ndvUG12N19WJHAO3c2hiUaefahi0bvAHw9d5Nyxxm8GWmb+N055ESJYpzOw/nQVP34RwcWnL16nVu\n3Pg33fDca2hdl9ir1zPtY39csuxjF1cHNm/Qtmm+3rtp9ZJ9/OjRY8IPHQO07cWZqGgsy5c1WmYb\nm3rZjr1sddnNKdOx9+/qcnj4cZL+SjZazsxMde7I3NewyNTXyCtTHHsAH3fvxJyZSwBQVZW/jLi/\nrbO0cTu2+eGWZR+7ujmyYb121qb3zkD9PjaoF0UKG+xHjbk5RYsWQaPRULRoURKN1CcqiOfqgnLN\nl1nBO1cXrLy5ZVO/DqVKlsjXDFnVavABt2LjiL+RwNPUp4T47KV12xYGZRJuJXL5wlXU9PwZaLS2\nqcu1zOeQ7f64uBu2ya5ujmzaoG3ffLyDaJ3lHBIb+99eMwlhTP9XBtoBAgE33b8/BjZmPKEoShlF\nUbwVRTmjKMpRRVEy1sWwBG5llFNV9YyuvKIoynRFUc4pinJWUZRs08sURSmqKMom3WtuBopmLZNX\n5a0suXUzQf97XFwiVlkGmctblePWLW2ZtLQ0Uu7dM+myCQBW5ctx61Z8plwJlLcql63MTV2ZtLQ0\nUlK0ubR5Dbe10nWoVFUlMGAjx44G0ueLT3P82717f0TQ7n15ym9p+Q4JcYn63xPjb2Np+Y5BmXKZ\nyqSlpXHv3n3ezPJVbY+O7fDZ8cIvQeSJKfZz1aqVuXPnLsuXzSLi+G4WL5pOsWJGr7qvbd19Eauc\n9plV2eeW0daLv/8fe/cdFtXRt3H8e3YBe4nGKGDHHmOJYFfEroBgzxM10UQTNYm9JfaoKZqiJjF2\nrLFXmr13UMECltgpVsBupJz3j10WFtAIuytu3t/nuXJdeXbnrHfGOTOzs+fMeWnmhIQEBgwYTXDw\nNq5cCaZy5fL4+Kw0LWeqv3OAiMhoHF6xXTg4ZHCs/vzTaDQEB20jOvIUO3fu41jQSUO5+fN+IfJG\nCJUqluP3PxZmPbtDUSIijevYPl0dp5R5UR17e7fhVOhZnj9/TkJCAoMGjuFY0BYuXT5GpUrlLLKA\nBmDvUJTIiJR2HRV5M8P8kana9YP7jyhU+C1y587FwMGf8+P3r/cLgzVkdkjVF4C+r0vzRftVzr32\n7dsSqm8XqRUokJ+2bZuze/dBs2W2t3+HqFTjSHTULezti6YpU5SoyJR6ffjgIYUKFaRsudKowIp1\n89i2dx1fvKZthCxx/gGMGzeUmTPn8+SJ8Q+NprJP0y6iIqNxSFvHDkWNxpEHDx5SSJ/X2bk6R4O2\ncPhYIIMGjDEsSv0wdSzjRv9g+HHDnIo5pPyd6zLfpFi6dvEOkRm0C4CSpYqzY/96NvgvpU493bZu\nCQkJjBwykT2HNnPq/D4qVHRi+ZK1Wc5oqTlcsq5dvFi1aqPRa/379eLE8e3Mm/szBQsWyHJ20Pdp\nqc69qMj0514x+6JGdfzgwUMKFdK1i5KlirNr/wY2+S+lrr6OU8tfIB8t27ixf+9hk3Km5uBQjIjI\nNHOitHXuUNRQRjcnSjn3nF1qEBS8laNBWxg4cLShLVuSpcYOjUbDvkObuXDlKHt2HeB4cKhZ8lri\n3MtfQLe4NnL0QLbvW8e8xdMpUqSwWfKCcf2Brl2krWN7h2Jp6jilj6vlXJ0jQYEcOhrA4IFjSUxM\nJDr6Fr/NnM+Z8P1cuHSYBw8esmvXATPltc6x2hq+86VmbWO1teX9LyhSrAi3o1IuyrgdfYci9kVe\n+Xi7HHb4BM5hvu8sGrduaImI2NsXM/S3oB9DMpjDRUakOveMxpDPmJrBGKKqKus2+rBr3wY+7mWZ\nO0CFMIf/0kL7SuADRVFyAtWAo6nemwicVFW1GvANsET/+h/AAkVRdiuKMlpRlOQNVDsANYDqQHNg\nmqIoaS+j7gc80X/mFCD9bB1QFOUzRVGCFUUJTkp8nKn/IEVJ/1raKz+UDAqZ6+qQF3mVPzPjMi8/\n1rWJN7XrtMbDszv9+vWkYUPjKwpGjRpAQkICf/1l4n5cWc6fUqZmrfd49vQp58P/Ni3LS1iinm20\nWmrWfI85c5bgUrsVjx8/YcQI07biycib2nZfJuv1/eLMNjY2fPZZD+rWbUuZMs6cPh3OiBHp98N9\nPTlffmxSUhLOLi0pVcYZF+eavPtuRUOZ3n2GUKLU+4Sfu0iXzu0smv3fzs/KlcszafIovvpKt9WQ\njY0Nffp0p349d5zK1ubMmXMMG55+f31zMCX/qNED+fMPH8PVfK+LNWQ2R39RuXJ5Jk8exZdffm1U\nRqvVsnjxb8ya5cPVqzfME/hFeXi189BGq6VO3ff5os9wvFp3o41Hcxo2rmu2bC9iifOvWrUqlHUq\nhe9m8++f+krtggwLARAcHEodl9Y0aezN0GH9yJHDjtatm3L3zj1CQjLcBdBkGWXmleoYbt28zfvv\nNqV5ow6MH/0Df87/ibz58mBjY0PPTz+gWeP2VKvYmLCzFzLc6/TVM1pmDAHdftYeHi1Zuy5l58Y5\nc5ZQsVJ9ajm3JPrmbaZNHZfl7C/O9mr9xa2bt6n5rhtNG7Vn7OgfmD3/Z/Lmy2Moo9VqmbvgF+bP\nXsq1qxHpPiM7MgMEB4Xg4twK10ZeDB3W36x7br+IpcaOpKQkGtdvx7sVG/K+c3UqVymfrkzW8mbw\noqnnnlaLY3F7jh09QYvGHQk+FsL4ySPSfUbWM2ec518iG/4ejgeHUtelDW6u7RkytC85cthRsGB+\n3N2bU61qEyqWq0/u3Lnp0tXLTHlfnCWlzJs1VlvLd77M5NEXemmZ1ztWW1fe/4JX6u9ewtulC73a\nfM64LyYxeOKXOJZy+PeDMsmU/mLU6AH8+XvGY0ibFh/g1sibLh0+5dM+3ajXwMVsmUV66n/gf9nl\nP7PQrr8avTS6q9nT/uTcEFiqL7cLKKwoSgFVVbcCZYF5QCXgpKIoRfTlV6iqmqiq6i1gL5D2LG4M\nLEv1Z596Qa65qqo6q6rqrNHmyajIC0VERlO8RMr6vqNjMaKjbqYvU1xXRqvVUiB/frPe1piRyIho\nihdP6ZAdHe2JSnNbYmRENCX0ZbRaLQUK5CcmJlaf1/jY6CjdscnbPdy5c4+NmwJxSbVvY48enXFv\n25yPPjJ9UTg66hb2qa7AKOZQlJs377ywjFarJX/+vMTF3je8365DGzamuoXQEixRzxGR0URERBuu\nVl633p+aNd4ze/Y3te2+TGRGdRZ9+4VldO0i30szV69eBYDLl68BsG6dH3XrZvib3KvnTPV3DlDc\n0T7dVikvaheRkRkcG2V87P37D9i77xCt0uyRmZSUxJo1m+nQ3p2sioy8SXFH4zq+maaOo1KVSVvH\nDo7FWLFyDn16D+HKlesAVNPXcfL/X7/O3+Q6fpGoyJs4Fk9p1w6OxTLM75iqXecvkJfYmDicXaoz\ncdIIQs/uoV//ngwZ1o8+n/ewSE5ryxwZedPQF4C+r0vTLl927jk6FmPVqrn0TtUukv3xxw9cunSF\n33/P+p0YGYmKumV0Ja+9Q9H09RqVciePVqslX/58xMbGERV1i8MHg4iJiePp02fs3L7P0I4tyRLn\nX+0671Oz5nuEhR9gx841lCtfhsAtpt21Y5TFqO3aE30zfR2nHkcy6pMvnL/E48dPqFKlInXq1aKN\nezNOh+3DZ/FMGrvWY96CX8ySFyA68pbR3VsOjsXSbXcWHXULxwzaxfPn8cTG6rKfCjnL1Ss3cCpX\nhqrVKgFw7Ypu8WnzhkCc67x8y72XsdQcDqB1azdOnjzN7dspWyrcvn2XpKQkVFVlwYLlJu/JHRV5\n0+gqWgfHohnU8U2jOs7/wjq+jlO5MobjfpkxicuXrjLnz8UmZUwrMjLaaNtDR8di6cftyJuGMro5\nUfq2fP78JZ48fkKVVD+EW4qlx44H9x9yYP9Rs+2Fb4lzLyYmjiePnxDgux0A341beM+MfXVkmjp2\ndCyWbpuX9HWcL912QRfOX+Lxk6dUqVKRJm4NuHb1BvfuxpCQkIDv5q3Uqfu+2fJa21htLd/5UrO2\nsdra8v4X3I6+wzsOKVewv2NfhDs3X30ro7u37gEQdT2aE4dCqFDVPD94phaVahyGjPtkXf+W6tzT\njyG1nKszYdIIQs7spm//ngwe2pfe+mfTJH/G3bsx+Ptup1atagjxJvrPLLTrbQZ+ItW2MXoZ/u4H\noKpqjKqqf6mq2gMIQreAnlH5jFj0J5Lg4FDKlStD6dIlsLW1pUsXL/z8thuV8fPbTo8enQHo2MGd\nPXvMd7vdiwQFhxjl6trFCz+/bWlybUvJ1dGd3fpcfn7b6NrFCzs7O0qXLkG5cmU4FnSS3LlzkTev\n7oeI3Llz0aK5K2fP6vavbdmyCcOG9ad9h548fWr67WOhJ85QpmxJSpR0xNbWBq8Obdi+xXg7mu2B\nu+n8ge4KEHevloa9O0H366uHV0s2r7fspMsS9Xzr1h0iIqKoUEH3gKimTRsSHn7B7Nnf1Lb7Mmkz\nd+7smWHm7t07AdChQ1v27Dn00s+MirpFpUrlefvtQgA0a9aIc+dMuyImbbvo0sUL3zTtwvcF7cLX\nbxtdMmgXb79diAIF8gOQM2dOmjVtxHn9gwKdnEobPtfDvQXnz2c9//HjoTiVK02pUsWxtbWlUydP\n/P2N69g/YDvduncEdLcX792rq+MCBfKzfp0P48dN5ciR44byUVE3qVQ5pY6bNmvIeRPr+EVOHD+F\nk1MpSurzd+jkTmDATqMyWwJ28r9u7QHwat+afXt1Dxdu2/J/VH+3CdXfbcKfsxbxy09/Mm/O0nR/\nxv/HzMnnXqlSKedeunbhv4Nu3XTtokOHNO1ivQ/jxk3l8OFgo2PGjx9GgQL5GDZsotkzh5w4TVmn\nUpQs5YitrS3eHduyLdB4HNkWuJsu/9ONIx5erTi4T1eve3YeoPK7FQ176NZr4GKRB3OmZYnzb/68\nZZRzqkOVyg1p3qwzf1+8QpvW5nlA3PHjpyjrlJK3YycPAvyNH+oV4L+T/+nbhXf7NuzVb/dRqlRx\nwwMjS5RwoHyFsly7HsHE8dOoXKEB71VpTK+PB7Bv72H6fDrELHkBTqZtFx3asjXA+OGfWwN20eVD\nbwA8vVtxQN8uChd+y/DQ8lKli1PWqRTXrt4gOuo2FSo6GW6zd3Wrz8Xzl7Oc0RJzi2Rdu3qn2zam\nWLGUbRq8vdoY5nZZdfLEaco4lTb0ad4d3NmSpo63BOyi64e6Pu3ldVyaa/qrZ78eM4j8BfIyetR3\nJuXLyPHjp9Kde+nacsCOVOfei9qyI+UrlOX6NfNdbf8ilhg7Cr9dyLAdS86cOWjiVp+LF7LellOz\nxLkHsG3Lbho0qg1AI9d6Zu2rdXWc0i46dPIgIE0dBwTs5MNuugf+ebdvw74X9XHly3DtegQ3bkTh\nXLsGuXLlBMC1SX3DfM5U1jhWW8t3vtSsb6y2rrz/BeEh5ylRpjj2JYphY2tDC6+m7N/28u+kyfIV\nyIutnS0ABQoVoJpLVa5cuGr2jCeOn6ZsqrG6Q0d3tvgb92+BATv54ENd/+bl3Zr9+jHEvdWH1Kjq\nRo2qbsyetYhff57N/LnL0q0XuTVrSHiY+dcxhDAHm+wOYGYLgfuqqp5WFKVJqtf3Ad2ASfrX76qq\n+kBRlKbAEVVVnyiKkg9wAq7ry3+uKMpioBC6xffhQM4MPnO3oihV0W1XY1aJiYkMGjQWf7/laLQa\nFi9aRVj4BcaPG8bxE6H4+W3Hx2cli3xmEBZ2gNiYOLr3SNky4cL5w+TPnw87O1vaebbC3f1Dws+Z\n/kCJxMREBg4ag7//X2g1GhYtXkVY2AXGjx/G8eO6XAt9VrJo0UzCww4QGxtHt+66XGFhF1iz1pdT\nobtJSExkwMDRJCUlUbRoEdauWQCA1kbLypUb2bZtDwAzpk8mR44cbAnU/ZJ99OgJvvgy68+tTUxM\nZOyI71i+dg4arZZVyzdw4dwlhn39BaEnz7J9yx5WLlvPjNnfcyA4gLjY+/TvnfIU7Lr1nYmOupXu\ni87oCUPw7tSWXLlzEnRmByuWrueXH2eZlNPc9QwwaPBYliz+DTs7Wy5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HZs/78y8T6NTh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MvuCJn2MPFZdkfIlOUF8mZ3hEyLjM2X3REyrWbj29kdIVN+O+aY3REyLUHJ7gSZE5jnRnZHyLSn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\nQvoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf00df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(28, 18))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('house_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GrLivArea and TotRmsAbvGrd = 0.83\n",
      "TotalBsmtSF and 1stFlrSF = 0.80\n",
      "OverallQual and SalePrice = 0.80\n",
      "GrLivArea and SalePrice = 0.73\n",
      "2ndFlrSF and GrLivArea = 0.71\n",
      "BedroomAbvGr and TotRmsAbvGrd = 0.68\n",
      "TotalBsmtSF and SalePrice = 0.65\n",
      "GarageCars and SalePrice = 0.64\n",
      "GrLivArea and FullBath = 0.63\n",
      "1stFlrSF and SalePrice = 0.63\n",
      "2ndFlrSF and TotRmsAbvGrd = 0.62\n",
      "2ndFlrSF and HalfBath = 0.61\n",
      "OverallQual and GarageCars = 0.60\n",
      "YearBuilt and YearRemodAdd = 0.59\n",
      "OverallQual and GrLivArea = 0.59\n",
      "OverallQual and YearBuilt = 0.57\n",
      "FullBath and SalePrice = 0.56\n",
      "FullBath and TotRmsAbvGrd = 0.55\n",
      "OverallQual and FullBath = 0.55\n",
      "OverallQual and YearRemodAdd = 0.55\n",
      "OverallQual and TotalBsmtSF = 0.54\n",
      "TotRmsAbvGrd and SalePrice = 0.54\n",
      "GrLivArea and BedroomAbvGr = 0.54\n",
      "YearBuilt and GarageCars = 0.54\n",
      "YearBuilt and SalePrice = 0.53\n",
      "1stFlrSF and GrLivArea = 0.53\n",
      "YearRemodAdd and SalePrice = 0.51\n",
      "2ndFlrSF and BedroomAbvGr = 0.51\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "size = data.shape[1]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据的特征间相关度分析做完之后可以继续处理，还有分析特征和目标的相关度，相关度高的就要留下来。特征间相关度越低越好，说明有差异性能互补；特征和目标间越高越好，说明有关。如车库大小和车库数量这俩强相关的，GrLivArea 与TotRmsAbvGrd强相关（0.83），GrLivArea与目标变量SalePrice 也强相关（0.8）,但与TotRmsAbvGrd与目标变量SalePrice相关度低（0.54），可删除TotRmsAbvGrd。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(['TotRmsAbvGrd'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = data['SalePrice'].values\n",
    "X = data.drop('SalePrice', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理／特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#y_train = ss_y.fit_transform(y_train)\n",
    "#y_test = ss_y.transform(y_test)\n",
    "\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 确定模型类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 尝试缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.13103889e-02,   8.85204676e-02,   2.58138940e-01,\n",
       "          8.17820226e-02,  -1.48910849e-01,  -3.68993583e-02,\n",
       "          6.39915322e-02,   1.49951080e-01,  -1.36675335e+11,\n",
       "         -1.62101045e+11,  -1.70309683e+10,   1.86216130e+11,\n",
       "          7.09838867e-02,   3.66210938e-04,  -7.98034668e-03,\n",
       "         -7.51113892e-03,  -1.12276077e-01,  -4.25109863e-02,\n",
       "          1.42211914e-02,   5.65834045e-02,   2.43148804e-02,\n",
       "          1.22680664e-02,  -8.73184204e-03,   3.10192108e-02,\n",
       "         -1.09901428e-02,  -6.34002686e-03]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测，下面计算score会自动调用predict\n",
    "lr_y_predict = lr.predict(X_test)\n",
    "lr_y_predict_train = lr.predict(X_train)\n",
    "\n",
    "#显示特征的回归系数\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The value of default measurement of LinearRegression on test is', 0.8672940231126719)\n",
      "('The value of default measurement of LinearRegression on train is', 0.852682412341351)\n"
     ]
    }
   ],
   "source": [
    "# 使用LinearRegression模型自带的评估模块（r2_score），并输出评估结果\n",
    "#测试集 \n",
    "print('The value of default measurement of LinearRegression on test is',lr.score(X_test, y_test))\n",
    "#训练集\n",
    "print('The value of default measurement of LinearRegression on train is',lr.score(X_train, y_train))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pMIa3JEmFMbwlqQAj4RSnmqfRn6fhLUkj3Lhx41i9erUBvo3o+T7vcePGDXobXm0uSSPc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda38828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - lr_y_predict_train,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "残差分布和高斯分布比较匹配"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HXvc6iDhuBVap6kjgWHI3zpgswTikqk+pajD8cj0w2Mt4wr4KvKmqb6tqM/Ag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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda38780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, lr_y_predict_train)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.04196818,  0.08821851,  0.258622  ,  0.08170266, -0.14852224,\n",
       "       -0.03709415,  0.06494286,  0.14927277,  0.14442755,  0.16799318,\n",
       "       -0.00797082,  0.25151313,  0.06977814,  0.00074068, -0.00710697,\n",
       "       -0.00760681, -0.11094249, -0.04190391,  0.01326893,  0.05693376,\n",
       "        0.02365101,  0.0119737 , -0.00854831,  0.03082883, -0.01042144,\n",
       "       -0.00708646])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性模型，随机梯度下降优化模型参数\n",
    "# 随机梯度下降一般在大数据集上应用，其实本项目不适合用\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "# 使用默认配置初始化线\n",
    "sgdr = SGDRegressor(max_iter=1500)\n",
    "\n",
    "# 训练：参数估计\n",
    "sgdr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "#sgdr_y_predict = sgdr.predict(X_test)\n",
    "\n",
    "sgdr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The value of default measurement of SGDRegressor on test is', 0.86784225114818869)\n",
      "('The value of default measurement of SGDRegressor on train is', 0.85266303012761968)\n"
     ]
    }
   ],
   "source": [
    "# 使用SGDRegressor模型自带的评估模块，并输出评估结果\n",
    "print('The value of default measurement of SGDRegressor on test is', sgdr.score(X_test, y_test))\n",
    "print ('The value of default measurement of SGDRegressor on train is', sgdr.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 正则化的线性回归（L2正则 --> 岭回归）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=[0.01, 0.1, 1, 10, 20, 40, 80, 100, 1000, 10000], cv=None,\n",
       "    fit_intercept=True, gcv_mode=None, normalize=False, scoring=None,\n",
       "    store_cv_values=True)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10,20, 40, 80,100,1000,10000]\n",
    "reg = RidgeCV(alphas=alphas, store_cv_values=True)   \n",
    "reg.fit(X_train, y_train)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k1QBrgGXAQuDTkhaWtqrz9j2GxvPjO4Hfj4gFwAeAL1Tw/yengJsj4hrgWmCp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\n49vgE8klAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10929860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 20.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[  4.16608846e-02,   8.74607950e-02,   2.54805746e-01,\n",
       "          7.74145708e-02,  -1.37120705e-01,  -4.23256954e-02,\n",
       "          6.54937276e-02,   1.49138905e-01,   1.41380764e-01,\n",
       "          1.58151068e-01,  -8.10938506e-03,   2.40696690e-01,\n",
       "          7.16621633e-02,   2.27635229e-04,   1.24524151e-03,\n",
       "         -7.87580796e-04,  -1.05541270e-01,  -4.33427485e-02,\n",
       "          1.81434860e-02,   6.03083339e-02,   2.57641322e-02,\n",
       "          1.34593167e-02,  -9.06135783e-03,   3.06098110e-02,\n",
       "         -1.06321549e-02,  -6.30444404e-03]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(reg.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "plt.plot(np.log10(reg.alpha_)*np.ones(3), [0.1, 0.2, 0.30])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', reg.alpha_)\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The value of default measurement of RidgeRegression is', 0.86664076202927087)\n"
     ]
    }
   ],
   "source": [
    "# 使用LinearRegression模型自带的评估模块（r2_score），并输出评估结果\n",
    "print('The value of default measurement of RidgeRegression is', reg.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 正则化的线性回归（L1正则 --> Lasso）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100, 1000], copy_X=True, cv=None,\n",
       "    eps=0.001, fit_intercept=True, max_iter=1000, n_alphas=100, n_jobs=1,\n",
       "    normalize=False, positive=False, precompute='auto', random_state=None,\n",
       "    selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = [0.001,0.01, 0.1, 1, 10,100,1000]\n",
    "\n",
    "lasso = LassoCV(alphas=alphas)   \n",
    "lasso.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d9f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.01)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.03087492,  0.08581511,  0.26779104,  0.06615757, -0.13458936,\n",
       "       -0.04030117,  0.06055645,  0.14998966,  0.01009124,  0.        ,\n",
       "       -0.0126059 ,  0.40747952,  0.06852639,  0.        ,  0.        ,\n",
       "        0.        , -0.08858223, -0.03786808,  0.01668903,  0.05959947,\n",
       "        0.02023942,  0.00731003, -0.00129964,  0.02313185, -0.        , -0.        ])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "lasso.coef_  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The value of default measurement of Lasso Regression on test is', 0.86520727952793175)\n",
      "('The value of default measurement of Lasso Regression on train is', 0.85134808654980687)\n"
     ]
    }
   ],
   "source": [
    "# 使用LinearRegression模型自带的评估模块（r2_score），并输出评估结果\n",
    "print ('The value of default measurement of Lasso Regression on test is', lasso.score(X_test, y_test))\n",
    "print ('The value of default measurement of Lasso Regression on train is', lasso.score(X_train, y_train))"
   ]
  },
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   "source": []
  }
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